In [5]:
import pandas as pd
from PIL import Image
from numpy import asarray
import numpy as np
import matplotlib.pyplot as plt
import os
#!pip install pyexcel
import math


i = 0
X_values = [] * 500
while i <= 100: 
      i += 0.2
      X_values.append(i)
      


count = 0
Y_values = [] * 500
while count <= 499:
    Y_values.append(1/X_values[count])
    count += 1

print('\n')
print('\n')
plt.figure(figsize=(20,10))
plt.xlabel('X_values')
plt.ylabel('Y_values')
#plt.plot(X_values, Y_values)
plt.scatter(X_values, Y_values)
plt.show()

count = 0
Test_Xvalues = [] * 100
Test_Yvalues = [] * 100
Training_Xvalues = [] * 400
Training_Yvalues = [] * 400

while count <= 499:
      if count % 5 == 0:
            Test_Xvalues.append(X_values[count])
          
            Test_Yvalues.append(Y_values[count])
      else:
        Training_Xvalues.append(X_values[count])
       
        Training_Yvalues.append(Y_values[count])    
     
      count += 1
        
x1 = len(Test_Xvalues)
x2 = len(Test_Yvalues)
x3 = len(Training_Xvalues)
x4 = len(Training_Yvalues)
x5 = len(X_values)
print('\n')
print(x1)
print(x2)
print(x3)
print(x4)
print(x5)

print('\n')
print('\n')
#plt.figure(figsize=(20,10))
plt.xlabel('Training_Xvalues')
plt.ylabel('Training_Yvalues')
plt.scatter(Training_Xvalues, Training_Yvalues)
plt.show()

print('\n')
#plt.figure(figsize=(20,10))
plt.xlabel('Test_Xvalues')
plt.ylabel('Test_Yvalues')
plt.scatter(Test_Xvalues, Test_Yvalues)
plt.show()



########################################################
#How to initialize a one d-array
#Y_values = []
#Y_values = [0 for i in range(500)] 

#How to initialize a 2-d array
#t = [ [0]*4 for i in range(5)]
#row = 0
#col = 0
#while row < 5:
#    col = 0
#    while col < 4:
#        t[row][col] = 1
#        print(col)
#        col += 1
#    row += 1
#print('\n')
#print(t)
#the first value 2 is the col and the second value 10 is the row
#x = [[0 for i in range(2)] for j in range(10)]
#print("The count is "  + str(count) + " that is count")
#print("Please, what is the number")
#input_a = input()
#print(t)
# string input 
#input_a = input() 
  
# print type 
#print(type(input_a))   
# integer input 
#input_b = int(input())   
# print type 
#print(type(input_b))


######################################################################




#NodesOfInputs = number of nodes in the input layer
#NodesOfOutputs =number of nodes in the output layer
#NumberOfHiddenLayer = number of Hidden layer
def gradient_descent(NodesOfInputs, NodesOfOutputs, NumberOfHiddenLayer):
    print('\n')
    
    #This create array with the size of the hidden layer
    #We want each index of the array to be a hidden layer and the number is carries 
    # the node of a hidden layer
    #For example index 0 stands for hidden layer 1 and the number it holds the nodes in hidden layer 1
    Hidden = []
    Hidden = [0 for i in range(NumberOfHiddenLayer)] 
    
    #InputNodes = 1
    
    print("print out the input node")
    print(NodesOfInputs)
    
    
    #Find the number of nodes for each hidden layer
    count = 0
    while count < NumberOfHiddenLayer:
        print("Please Enter the Nodes of Hidden Layer " + str(count))
        Nodes = int(input()) 
        Hidden[count] = Nodes
        count += 1
    
    #Find the Total number of bias
    count = 0
    bias = 0
    bias = NodesOfOutputs
    while count < NumberOfHiddenLayer:
        if count == 0:
            bias = NodesOfOutputs
            bias += Hidden[count] 
        bias += Hidden[count]
        count += 1
    
    #create an array for the bias and initialize to 0
    BiasArray = []
    BiasArray = [0 for i in range(bias)] 
    
    print("print out outputnodes")
    print(NodesOfOutputs)
    print("print out the array with all the bias")
    print(BiasArray)
    
    #Find the Total number of weights
    count = 0
    weights = NodesOfInputs
    hold1 = NumberOfHiddenLayer - 1
    while count < NumberOfHiddenLayer:
        weights += weights * Hidden[count]
        if count == hold1:
            weights += weights * NodesOfOutputs
        count += 1
    print("print out the weights")
    print(weights)
    #create an array for the weights and initialize to 0.5
    WeightsArray = [0.5 for i in range(weights)] 
    
    count = 0
    hold1 = 0
   
    
    length = len(Training_Xvalues)
    HoldArray = Training_Xvalues
    
    #create a container which will hold all the values for each node
    container = [[0 for i in range(length)] for j in range(bias)]
    
    
    #keep track of which node 
    N = 0
    #to keep track of the weights
    W = 0
    #to keep track of the bias
    B = 0

    # If the number of hidden layer is eqaul to zero
    if NumberOfHiddenLayer == 0:
            
        #iterate through each node of the output layer
        count6 = 0
        while count6 < NodesOfOutputs:
            
            hold2 = 1
            print("print count6")
            print(count6)
            #condition if the input has only one node
            if NodesOfInputs == 1:
                
                # transversing the entire array
                count7 = 0
                while count7 < length:
                    #The only time you transverse an output node, replace the value in the container
                    container[N][count7] = Training_Xvalues[count7] * WeightsArray[W]
                    hold1 = count6
                    count7 += 1    
                
                print("print out container")
                print(container)
                
                
              
                
            #condition if the input is greater than one node
            else:
                # Transversing each input Node
                count8 = 0
                while count8 < NodesOfInputs:
                    count9 = 0
                    # Transversing the length of the array or the column
                    while count9 < length:
                        # If you are in the first weight iteration of the output node, replace all the values in the container
                        if hold2 != count8:
                            container[N][count9] = Training_Xvalues[count9] * WeightsArray[W]
                            hold2 = count8
                        # if you are not in the first weight iteration just add the input weights to the current values
                        else:
                            container[N][count9] += Training_Xvalues[count9] * WeightsArray[W]
                        count9 += 1
                    #increment the current weight as the input Node changes
                    W += 1
                    count8 += 1
                    hold2 += 1
            #increment the output node, count 6, the weight, and the current node
            count6 +=1
            W +=1
            N +=1
            
    
    
    
    
    
    
    
    
    #iterate through the hidden layers
    count1 = 0
    while count1 < NumberOfHiddenLayer:
        
        
      
        #########################iterate through the node of each hidden layer
        
        count2 = 0
        M = Hidden[count1]
        while count2 < M:
            
                hold4 = 1
                hold5 = 1
                hold7 = 1
                #condition if the input has only one node
                if NodesOfInputs == 1:
                    # first iteration through the hidden layers
                     if count1 == 0:
                        #transversing the entire array        
                        count3 = 0
                        while count3 < length:
                            container[N][count3] = Training_Xvalues[count3] * WeightsArray[W]
                            count3 += 1
            
                     #When the hidden layer is at the last layer before the output layer
                     if count1 == NumberOfHiddenLayer-1:
                         count10 = 0
                         # going through each node in the output layer
                         while count10 < NodesOfOutputs:
                            count4 = 0
                            hold3 = 1
                             # going through each node in the last hidden layer, before the output layer
                            M = Hidden[count1] 
                            while count4 < M:
                                  count3 =0
                                  # tranversing the array of the inputs
                                  while count3 < length:
                                     #For every first iteration of the node in the output layer, replace the current values
                                     if hold3 !=count4:
                                         container[N][count3] = Training_Xvalues[count3] * WeightsArray[W]
                                         hold3 = count4
                                     else:
                                         container[N][count3] += Training_Xvalues[count3] * WeightsArray[W]
                                     count3 += 1
                                  count4 += 1
                                  hold3 += 1
                                  W += 1
                            count10 += 1
                            W += 1
                            N +=1
            
                     #When you still have multiple remaining hidden layers
                     else:
                         count10 = 0
                         # Iterate through the nodes of the previous hidden layer
                         M = Hidden[count-1]
                         while count10 < M:
                                 count3 = 0
                                 #transversing the entire array for each node of the hidden layer
                                 #and adding the values of weights of the previous node to that node
                                 while count3 < length:
                                     if hold4 != count10:
                                         container[N][count3] = Training_Xvalues[count3] * WeightsArray[W]
                                         hold4 = count10
                                     else:
                                         container[N][count3] += Training_Xvalues[count3] * WeightsArray[W]
                                     count +=3
                                 count10 += 1
                                 hold4 += 1
                                 W +=1
                        

            
                #condition if the input is greater than one node
                else:
                    # going through the very first iteration
                    if count1 == 0:
                        # Transversing each input Node
                        count4 = 0
                        while count4 < NodesOfInputs:
                                count5 = 0
                                # Transversing the length of the array or the column
                                while count5 < length:
                                    # If you are in the first input replace all the values
                                    if hold5 != count4:
                                        container[N][count5] = Training_Xvalues[count5] * WeightsArray[W]
                                        hold5 = count4
                                    # if you are not in the first input just add the input weights to the current values
                                    else:
                                        container[N][count5] += Training_Xvalues[count5] * WeightsArray[W]
                                    count5 += 1
                                #increment the current weight as the input Node changes
                                W += 1
                                count4 += 1
                                hold5 += 1
               
                    
                     #When the hidden layer is at the last layer before the output layer
                    if count1 == NumberOfHiddenLayer-1:
                        count10 = 0
                        while count10 < NodesOfOutputs:
                            count4 = 0
                            hold6 = 1
                            while count4 < Hidden[count1]:
                                count3 =0
                                while count3 < length:
                                    if hold6 != count4:
                                        container[N][count3] = Training_Xvalues[count3] * WeightsArray[W]
                                        hold6 = count4
                                    else:
                                        container[N][count3] += Training_Xvalues[count3] * WeightsArray[W]
                                    count3 += 1
                                count4 += 1
                                hold6 += 1
                                W += 1
                            count10 += 1
                            W += 1
                            N +=1
                    
                    # When you still have multiple remaining hidden layers
                    else:
                        count10 = 0
                        # Iterate through the previous value nodes
                        while count10 < Hidden[count-1]:
                            count3 = 0
                            #transvering the entire array for each node of the hidden layer
                            # and adding the values of weights of the previous node to that node
                            while count3 < length:
                                if hold7 != count10:
                                    container[N][count3] = Training_Xvalues[count3] * WeightsArray[W]
                                    hold7 = count10
                                else:
                                    container[N][count3] += Training_Xvalues[count3] * WeightsArray[W]
                                count +=3
                            count10 += 1
                            hold7 += 1
                            W +=1
                
                
                #increment the current Node as you switch to the next node in the hidden layer
                #increment the current weight as you switch to the next node in the hidden layer
                N += 1
                W += 1
                print(container)
                count2 += 1
        count1 += 1
    print("print out the container")
    print(container)
       
        
    
gradient_descent(1, 1, 1)
        
       
        




100
100
400
400
500














    
    

















    
    






print out the input node
1
Please Enter the Nodes of Hidden Layer 0
1
print out outputnodes
1
print out the array with all the bias
[0, 0, 0]
print out the weights
4
[[0.2, 0.6000000000000001, 0.8, 1.0, 1.4, 1.5999999999999999, 1.7999999999999998, 1.9999999999999998, 2.4, 2.6, 2.8000000000000003, 3.0000000000000004, 3.400000000000001, 3.600000000000001, 3.800000000000001, 4.000000000000001, 4.400000000000001, 4.600000000000001, 4.800000000000002, 5.000000000000002, 5.400000000000002, 5.600000000000002, 5.8000000000000025, 6.000000000000003, 6.400000000000003, 6.600000000000003, 6.800000000000003, 7.0000000000000036, 7.400000000000004, 7.600000000000004, 7.800000000000004, 8.000000000000004, 8.400000000000002, 8.600000000000001, 8.8, 9.0, 9.399999999999999, 9.599999999999998, 9.799999999999997, 9.999999999999996, 10.399999999999995, 10.599999999999994, 10.799999999999994, 10.999999999999993, 11.399999999999991, 11.59999999999999, 11.79999999999999, 11.99999999999999, 12.399999999999988, 12.599999999999987, 12.799999999999986, 12.999999999999986, 13.399999999999984, 13.599999999999984, 13.799999999999983, 13.999999999999982, 14.39999999999998, 14.59999999999998, 14.79999999999998, 14.999999999999979, 15.399999999999977, 15.599999999999977, 15.799999999999976, 15.999999999999975, 16.399999999999974, 16.599999999999973, 16.799999999999972, 16.99999999999997, 17.39999999999997, 17.59999999999997, 17.79999999999997, 17.999999999999968, 18.399999999999967, 18.599999999999966, 18.799999999999965, 18.999999999999964, 19.399999999999963, 19.599999999999962, 19.79999999999996, 19.99999999999996, 20.39999999999996, 20.59999999999996, 20.799999999999958, 20.999999999999957, 21.399999999999956, 21.599999999999955, 21.799999999999955, 21.999999999999954, 22.399999999999952, 22.59999999999995, 22.79999999999995, 22.99999999999995, 23.39999999999995, 23.599999999999948, 23.799999999999947, 23.999999999999947, 24.399999999999945, 24.599999999999945, 24.799999999999944, 24.999999999999943, 25.39999999999994, 25.59999999999994, 25.79999999999994, 25.99999999999994, 26.399999999999938, 26.599999999999937, 26.799999999999937, 26.999999999999936, 27.399999999999935, 27.599999999999934, 27.799999999999933, 27.999999999999932, 28.39999999999993, 28.59999999999993, 28.79999999999993, 28.99999999999993, 29.399999999999928, 29.599999999999927, 29.799999999999926, 29.999999999999925, 30.399999999999924, 30.599999999999923, 30.799999999999923, 30.999999999999922, 31.39999999999992, 31.59999999999992, 31.79999999999992, 31.99999999999992, 32.39999999999992, 32.59999999999992, 32.799999999999926, 32.99999999999993, 33.399999999999935, 33.59999999999994, 33.79999999999994, 33.99999999999994, 34.39999999999995, 34.59999999999995, 34.799999999999955, 34.99999999999996, 35.39999999999996, 35.599999999999966, 35.79999999999997, 35.99999999999997, 36.39999999999998, 36.59999999999998, 36.79999999999998, 36.999999999999986, 37.39999999999999, 37.599999999999994, 37.8, 38.0, 38.400000000000006, 38.60000000000001, 38.80000000000001, 39.000000000000014, 39.40000000000002, 39.60000000000002, 39.800000000000026, 40.00000000000003, 40.400000000000034, 40.60000000000004, 40.80000000000004, 41.00000000000004, 41.40000000000005, 41.60000000000005, 41.800000000000054, 42.00000000000006, 42.40000000000006, 42.600000000000065, 42.80000000000007, 43.00000000000007, 43.40000000000008, 43.60000000000008, 43.80000000000008, 44.000000000000085, 44.40000000000009, 44.600000000000094, 44.8000000000001, 45.0000000000001, 45.400000000000105, 45.60000000000011, 45.80000000000011, 46.000000000000114, 46.40000000000012, 46.60000000000012, 46.800000000000125, 47.00000000000013, 47.400000000000134, 47.600000000000136, 47.80000000000014, 48.00000000000014, 48.40000000000015, 48.60000000000015, 48.80000000000015, 49.000000000000156, 49.40000000000016, 49.600000000000165, 49.80000000000017, 50.00000000000017, 50.400000000000176, 50.60000000000018, 50.80000000000018, 51.000000000000185, 51.40000000000019, 51.60000000000019, 51.800000000000196, 52.0000000000002, 52.400000000000205, 52.60000000000021, 52.80000000000021, 53.00000000000021, 53.40000000000022, 53.60000000000022, 53.800000000000225, 54.00000000000023, 54.40000000000023, 54.600000000000236, 54.80000000000024, 55.00000000000024, 55.40000000000025, 55.60000000000025, 55.80000000000025, 56.000000000000256, 56.40000000000026, 56.600000000000264, 56.80000000000027, 57.00000000000027, 57.400000000000276, 57.60000000000028, 57.80000000000028, 58.000000000000284, 58.40000000000029, 58.60000000000029, 58.800000000000296, 59.0000000000003, 59.400000000000304, 59.60000000000031, 59.80000000000031, 60.00000000000031, 60.40000000000032, 60.60000000000032, 60.800000000000324, 61.00000000000033, 61.40000000000033, 61.600000000000335, 61.80000000000034, 62.00000000000034, 62.40000000000035, 62.60000000000035, 62.80000000000035, 63.000000000000355, 63.40000000000036, 63.600000000000364, 63.80000000000037, 64.00000000000037, 64.40000000000038, 64.60000000000038, 64.80000000000038, 65.00000000000038, 65.40000000000039, 65.60000000000039, 65.8000000000004, 66.0000000000004, 66.4000000000004, 66.6000000000004, 66.80000000000041, 67.00000000000041, 67.40000000000042, 67.60000000000042, 67.80000000000042, 68.00000000000043, 68.40000000000043, 68.60000000000043, 68.80000000000044, 69.00000000000044, 69.40000000000045, 69.60000000000045, 69.80000000000045, 70.00000000000045, 70.40000000000046, 70.60000000000046, 70.80000000000047, 71.00000000000047, 71.40000000000047, 71.60000000000048, 71.80000000000048, 72.00000000000048, 72.40000000000049, 72.60000000000049, 72.8000000000005, 73.0000000000005, 73.4000000000005, 73.6000000000005, 73.80000000000051, 74.00000000000051, 74.40000000000052, 74.60000000000052, 74.80000000000052, 75.00000000000053, 75.40000000000053, 75.60000000000053, 75.80000000000054, 76.00000000000054, 76.40000000000055, 76.60000000000055, 76.80000000000055, 77.00000000000055, 77.40000000000056, 77.60000000000056, 77.80000000000057, 78.00000000000057, 78.40000000000057, 78.60000000000058, 78.80000000000058, 79.00000000000058, 79.40000000000059, 79.60000000000059, 79.8000000000006, 80.0000000000006, 80.4000000000006, 80.6000000000006, 80.80000000000061, 81.00000000000061, 81.40000000000062, 81.60000000000062, 81.80000000000062, 82.00000000000063, 82.40000000000063, 82.60000000000063, 82.80000000000064, 83.00000000000064, 83.40000000000065, 83.60000000000065, 83.80000000000065, 84.00000000000065, 84.40000000000066, 84.60000000000066, 84.80000000000067, 85.00000000000067, 85.40000000000067, 85.60000000000068, 85.80000000000068, 86.00000000000068, 86.40000000000069, 86.60000000000069, 86.8000000000007, 87.0000000000007, 87.4000000000007, 87.6000000000007, 87.80000000000071, 88.00000000000071, 88.40000000000072, 88.60000000000072, 88.80000000000072, 89.00000000000072, 89.40000000000073, 89.60000000000073, 89.80000000000074, 90.00000000000074, 90.40000000000074, 90.60000000000075, 90.80000000000075, 91.00000000000075, 91.40000000000076, 91.60000000000076, 91.80000000000076, 92.00000000000077, 92.40000000000077, 92.60000000000078, 92.80000000000078, 93.00000000000078, 93.40000000000079, 93.60000000000079, 93.8000000000008, 94.0000000000008, 94.4000000000008, 94.6000000000008, 94.80000000000081, 95.00000000000081, 95.40000000000082, 95.60000000000082, 95.80000000000082, 96.00000000000082, 96.40000000000083, 96.60000000000083, 96.80000000000084, 97.00000000000084, 97.40000000000084, 97.60000000000085, 97.80000000000085, 98.00000000000085, 98.40000000000086, 98.60000000000086, 98.80000000000086, 99.00000000000087, 99.40000000000087, 99.60000000000088, 99.80000000000088, 100.00000000000088], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
print out the container
[[0.2, 0.6000000000000001, 0.8, 1.0, 1.4, 1.5999999999999999, 1.7999999999999998, 1.9999999999999998, 2.4, 2.6, 2.8000000000000003, 3.0000000000000004, 3.400000000000001, 3.600000000000001, 3.800000000000001, 4.000000000000001, 4.400000000000001, 4.600000000000001, 4.800000000000002, 5.000000000000002, 5.400000000000002, 5.600000000000002, 5.8000000000000025, 6.000000000000003, 6.400000000000003, 6.600000000000003, 6.800000000000003, 7.0000000000000036, 7.400000000000004, 7.600000000000004, 7.800000000000004, 8.000000000000004, 8.400000000000002, 8.600000000000001, 8.8, 9.0, 9.399999999999999, 9.599999999999998, 9.799999999999997, 9.999999999999996, 10.399999999999995, 10.599999999999994, 10.799999999999994, 10.999999999999993, 11.399999999999991, 11.59999999999999, 11.79999999999999, 11.99999999999999, 12.399999999999988, 12.599999999999987, 12.799999999999986, 12.999999999999986, 13.399999999999984, 13.599999999999984, 13.799999999999983, 13.999999999999982, 14.39999999999998, 14.59999999999998, 14.79999999999998, 14.999999999999979, 15.399999999999977, 15.599999999999977, 15.799999999999976, 15.999999999999975, 16.399999999999974, 16.599999999999973, 16.799999999999972, 16.99999999999997, 17.39999999999997, 17.59999999999997, 17.79999999999997, 17.999999999999968, 18.399999999999967, 18.599999999999966, 18.799999999999965, 18.999999999999964, 19.399999999999963, 19.599999999999962, 19.79999999999996, 19.99999999999996, 20.39999999999996, 20.59999999999996, 20.799999999999958, 20.999999999999957, 21.399999999999956, 21.599999999999955, 21.799999999999955, 21.999999999999954, 22.399999999999952, 22.59999999999995, 22.79999999999995, 22.99999999999995, 23.39999999999995, 23.599999999999948, 23.799999999999947, 23.999999999999947, 24.399999999999945, 24.599999999999945, 24.799999999999944, 24.999999999999943, 25.39999999999994, 25.59999999999994, 25.79999999999994, 25.99999999999994, 26.399999999999938, 26.599999999999937, 26.799999999999937, 26.999999999999936, 27.399999999999935, 27.599999999999934, 27.799999999999933, 27.999999999999932, 28.39999999999993, 28.59999999999993, 28.79999999999993, 28.99999999999993, 29.399999999999928, 29.599999999999927, 29.799999999999926, 29.999999999999925, 30.399999999999924, 30.599999999999923, 30.799999999999923, 30.999999999999922, 31.39999999999992, 31.59999999999992, 31.79999999999992, 31.99999999999992, 32.39999999999992, 32.59999999999992, 32.799999999999926, 32.99999999999993, 33.399999999999935, 33.59999999999994, 33.79999999999994, 33.99999999999994, 34.39999999999995, 34.59999999999995, 34.799999999999955, 34.99999999999996, 35.39999999999996, 35.599999999999966, 35.79999999999997, 35.99999999999997, 36.39999999999998, 36.59999999999998, 36.79999999999998, 36.999999999999986, 37.39999999999999, 37.599999999999994, 37.8, 38.0, 38.400000000000006, 38.60000000000001, 38.80000000000001, 39.000000000000014, 39.40000000000002, 39.60000000000002, 39.800000000000026, 40.00000000000003, 40.400000000000034, 40.60000000000004, 40.80000000000004, 41.00000000000004, 41.40000000000005, 41.60000000000005, 41.800000000000054, 42.00000000000006, 42.40000000000006, 42.600000000000065, 42.80000000000007, 43.00000000000007, 43.40000000000008, 43.60000000000008, 43.80000000000008, 44.000000000000085, 44.40000000000009, 44.600000000000094, 44.8000000000001, 45.0000000000001, 45.400000000000105, 45.60000000000011, 45.80000000000011, 46.000000000000114, 46.40000000000012, 46.60000000000012, 46.800000000000125, 47.00000000000013, 47.400000000000134, 47.600000000000136, 47.80000000000014, 48.00000000000014, 48.40000000000015, 48.60000000000015, 48.80000000000015, 49.000000000000156, 49.40000000000016, 49.600000000000165, 49.80000000000017, 50.00000000000017, 50.400000000000176, 50.60000000000018, 50.80000000000018, 51.000000000000185, 51.40000000000019, 51.60000000000019, 51.800000000000196, 52.0000000000002, 52.400000000000205, 52.60000000000021, 52.80000000000021, 53.00000000000021, 53.40000000000022, 53.60000000000022, 53.800000000000225, 54.00000000000023, 54.40000000000023, 54.600000000000236, 54.80000000000024, 55.00000000000024, 55.40000000000025, 55.60000000000025, 55.80000000000025, 56.000000000000256, 56.40000000000026, 56.600000000000264, 56.80000000000027, 57.00000000000027, 57.400000000000276, 57.60000000000028, 57.80000000000028, 58.000000000000284, 58.40000000000029, 58.60000000000029, 58.800000000000296, 59.0000000000003, 59.400000000000304, 59.60000000000031, 59.80000000000031, 60.00000000000031, 60.40000000000032, 60.60000000000032, 60.800000000000324, 61.00000000000033, 61.40000000000033, 61.600000000000335, 61.80000000000034, 62.00000000000034, 62.40000000000035, 62.60000000000035, 62.80000000000035, 63.000000000000355, 63.40000000000036, 63.600000000000364, 63.80000000000037, 64.00000000000037, 64.40000000000038, 64.60000000000038, 64.80000000000038, 65.00000000000038, 65.40000000000039, 65.60000000000039, 65.8000000000004, 66.0000000000004, 66.4000000000004, 66.6000000000004, 66.80000000000041, 67.00000000000041, 67.40000000000042, 67.60000000000042, 67.80000000000042, 68.00000000000043, 68.40000000000043, 68.60000000000043, 68.80000000000044, 69.00000000000044, 69.40000000000045, 69.60000000000045, 69.80000000000045, 70.00000000000045, 70.40000000000046, 70.60000000000046, 70.80000000000047, 71.00000000000047, 71.40000000000047, 71.60000000000048, 71.80000000000048, 72.00000000000048, 72.40000000000049, 72.60000000000049, 72.8000000000005, 73.0000000000005, 73.4000000000005, 73.6000000000005, 73.80000000000051, 74.00000000000051, 74.40000000000052, 74.60000000000052, 74.80000000000052, 75.00000000000053, 75.40000000000053, 75.60000000000053, 75.80000000000054, 76.00000000000054, 76.40000000000055, 76.60000000000055, 76.80000000000055, 77.00000000000055, 77.40000000000056, 77.60000000000056, 77.80000000000057, 78.00000000000057, 78.40000000000057, 78.60000000000058, 78.80000000000058, 79.00000000000058, 79.40000000000059, 79.60000000000059, 79.8000000000006, 80.0000000000006, 80.4000000000006, 80.6000000000006, 80.80000000000061, 81.00000000000061, 81.40000000000062, 81.60000000000062, 81.80000000000062, 82.00000000000063, 82.40000000000063, 82.60000000000063, 82.80000000000064, 83.00000000000064, 83.40000000000065, 83.60000000000065, 83.80000000000065, 84.00000000000065, 84.40000000000066, 84.60000000000066, 84.80000000000067, 85.00000000000067, 85.40000000000067, 85.60000000000068, 85.80000000000068, 86.00000000000068, 86.40000000000069, 86.60000000000069, 86.8000000000007, 87.0000000000007, 87.4000000000007, 87.6000000000007, 87.80000000000071, 88.00000000000071, 88.40000000000072, 88.60000000000072, 88.80000000000072, 89.00000000000072, 89.40000000000073, 89.60000000000073, 89.80000000000074, 90.00000000000074, 90.40000000000074, 90.60000000000075, 90.80000000000075, 91.00000000000075, 91.40000000000076, 91.60000000000076, 91.80000000000076, 92.00000000000077, 92.40000000000077, 92.60000000000078, 92.80000000000078, 93.00000000000078, 93.40000000000079, 93.60000000000079, 93.8000000000008, 94.0000000000008, 94.4000000000008, 94.6000000000008, 94.80000000000081, 95.00000000000081, 95.40000000000082, 95.60000000000082, 95.80000000000082, 96.00000000000082, 96.40000000000083, 96.60000000000083, 96.80000000000084, 97.00000000000084, 97.40000000000084, 97.60000000000085, 97.80000000000085, 98.00000000000085, 98.40000000000086, 98.60000000000086, 98.80000000000086, 99.00000000000087, 99.40000000000087, 99.60000000000088, 99.80000000000088, 100.00000000000088], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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0]]




















In [50]:



     


import pandas as pd
from PIL import Image
from numpy import asarray
import numpy as np
import math
import matplotlib.pyplot as plt
import os






#initializing random weights
w1 = 0.3
w2 = 0.4
w3 = 0.5
w4 = 0.2
w5 = 0.7
w6 = 0.6
w7 = 0.3
w8 = 0.1

#initialize the bias to be a constant 1
bias = 1



#this will save the X values at each node
array1 = [1, 2, 3, 4, 5]
array2 = [1, 2, 3, 4, 5]
array3 = [1, 2, 3, 4, 5]
array4 = [1, 2, 3, 4, 5]
array5 = [1, 2, 3, 4, 5]



#this will save the Y values at each node
Node1 = [1, 2, 3, 4, 5]
Node2 = [1, 2, 3, 4, 5]
Node3 = [1, 2, 3, 4, 5]
Node4 = [1, 2, 3, 4, 5]
Node5 = [1, 2, 3, 4, 5]



#intialize the learning rate
learningrate = 0.03


#initialize the actual Y value for the output
observe = [1, 2, 5, 8, 11]


#initialize the actual X value for the input
arr = [1, 2, 3, 4, 5]


#Graph the X and Y values 
print('\n')
plt.xlabel('Xvalues')
plt.ylabel('Yvalues')
plt.scatter(arr, observe)
plt.show()



# Train your data
epoch = 0
while epoch < 10000:
    

    # Weight1 multiplied by the values of the input array
    array1 = w1 * np.array(arr) + bias
    
    # Weight2 multiplied by the vales of the input array
    array2 = w2 * np.array(arr) + bias
    
    print(array1)
    


    count = 0
    while count < 5:
        # Node 1: takes in the input X and passes it through the sigmoid function then stores it at the corresponding Y
        Node1[count] = 1 / (1 + math.exp(-(array1[count])))
 
        # Node 2: takes in the input X and passes it through the sigmoid function then stores it at the correspodning X
        Node2[count] = 1 / (1 + math.exp(-array2[count]))
        count += 1

    
    
    
    array3 = w3 * np.array(Node1) + w4 * np.array(Node2) + bias

    array4 = w5 * np.array(Node1) + w6 * np.array(Node2) + bias

    count = 0
    # function 
    while count < 5:
        # Node 1
        Node3[count] = 1 / (1 + math.exp(-array3[count]))
    
        # Node 2
        Node4[count] = 1 / (1 + math.exp(-array4[count]))
        count += 1





    array5 = w7 * np.array(Node3) + w8 * np.array(Node4) + bias
    count = 0
    while count < 5:  
        # Node 5
        Node5[count] = 1 / (1 + math.exp(-array5[count]))
        count += 1
    # The predicted Y values are stores at node 5
    predicted = Node5
    
    

    
    
    

    # intiliazing a storage for each weight, which you will use to obtain the new weight in gradient descent
    w1old = w1
    w2old = w2
    w3old = w3
    w4old = w4
    w5old = w5
    w6old = w6
    w7old = w7
    w8old = w8    
    
    #gradient descent
    count = 0
    while count < 5:
        # Get the derivative for each node using it's correspoding X values
        s = 1/(1 + math.exp(-array1[count]))
        dernode1 = s*(1-s)
    
        s = 1/(1 + math.exp(-array2[count]))
        dernode2 = s*(1-s)
    
        s = 1/(1 + math.exp(-array3[count]))
        dernode3 = s*(1-s)
    
        s = 1/(1 + math.exp(-array4[count]))
        dernode4 = s*(1-s)
    
        s = 1/(1 + math.exp(-array5[count]))
        dernode5 = s*(1-s)
    
    #Adding the derivative for all the indexes using sum of sqaure Residuals
    #For the first iteration, replace all the weights at that index
        if count == 0:
            
            w1 = -2 * (observe[count] - predicted[count]) * arr[count] * dernode1 * w3 * dernode1 * w5 * dernode3 * w7 * dernode4 * w8 * dernode5

            w2 = -2 * (observe[count] - predicted[count]) * arr[count] * w4 * dernode2 * w6 * dernode2 * w7 * dernode3 * w8 * dernode4 * dernode5
    
    
            w3 = -2 * (observe[count] - predicted[count]) * Node1[count] * dernode3 * w7 * dernode5
    
            w4 = -2 * (observe[count] - predicted[count]) * Node2[count] * dernode3 * w7 * dernode5
    
            w5 = -2 * (observe[count] - predicted[count]) * Node1[count] * dernode4 * w8 * dernode5
    
            w6 = -2 * (observe[count] - predicted[count]) * Node2[count] * dernode4 * w8 * dernode5
    
    
            w7 = -2 * (observe[count] - predicted[count]) * Node3[count] * dernode5
    
            w8 = -2 * (observe[count] - predicted[count]) * Node4[count] * dernode5
        
       #For all the other iteration, add the weight to that index to get your final weights 
        else:
            

        
            w1 += -2 * (observe[count] - predicted[count]) * arr[count] * dernode1 * w3 * dernode1 * w5 * dernode3 * w7 * dernode4 * w8 * dernode5      

            w2 += -2 * (observe[count] - predicted[count]) * arr[count] * w4 * dernode2 * w6 * dernode2 * w7 * dernode3 * w8 * dernode4 * dernode5
    
    
            w3 += -2 * (observe[count] - predicted[count]) * Node1[count] * dernode3 * w7 * dernode5
    
            w4 += -2 * (observe[count] - predicted[count]) * Node2[count] * dernode3 * w7 * dernode5
    
            w5 += -2 * (observe[count] - predicted[count]) * Node1[count] * dernode4 * w8 * dernode5
    
            w6 += -2 * (observe[count] - predicted[count]) * Node2[count] * dernode4 * w8 * dernode5
    
    
            w7 += -2 * (observe[count] - predicted[count]) * Node3[count] * dernode5
    
            w8 += -2 * (observe[count] - predicted[count]) * Node4[count] * dernode5
        
        count += 1
    
    #Using gradient descent to find the where the slope equals to zero fast for each weight.

    stepsize = w1 * learningrate
    print("print out learning rate")
    print(learningrate)
    
    print("print out the slope")
    print(w1)
    
    print("print step size")
    print(stepsize)
    
    w1 = w1old - stepsize


    stepsize = w2 * learningrate
    w2 = w2old - stepsize

    stepsize = w3 * learningrate
    w3 = w3old - stepsize

    stepsize = w4 * learningrate
    w4 = w4old - stepsize

    stepsize = w5 * learningrate
    w5 = w5old - stepsize

    stepsize = w6 * learningrate
    w6 = w6old - stepsize

    stepsize = w7 * learningrate
    w7 = w7old - stepsize

    stepsize = w8 * learningrate
    w8 = w8old - stepsize
    
    epoch += 1




####################################################################################
#Using the same inputs to see if your algorithm is correct, then graph at the end using the observe X values with the final
 #predicted Y values



# Weight1 multiplied by the values of the input array
array1 = w1 * np.array(arr) + bias
# Weight2 multiplied by the vales of the input array
array2 = w2 * np.array(arr) + bias


count = 0
while count < 5:
    # Node 1
    Node1[count] = 1 / (1 + math.exp(-array1[count]))
    
    # Node 2
    Node2[count] = 1 / (1 + math.exp(-array2[count]))
    count += 1

    
    
    
array3 = w3 * np.array(Node1) + w4 * np.array(Node2) + bias

array4 = w5 * np.array(Node1) + w6 * np.array(Node2) + bias

count = 0
while count < 5:
    # Node 1
    Node3[count] = 1 / (1 + math.exp(-array3[count]))
    
    # Node 2
    Node4[count] = 1 / (1 + math.exp(-array4[count]))
    count += 1





array5 = w7 * np.array(Node3) + w8 * np.array(Node4) + bias
count = 0
while count < 5:  
    # Node 5
    Node5[count] = 1 / (1 + math.exp(-array5[count]))
    count += 1
    
predicted = Node5
    

print('\n')
#plt.figure(figsize=(20,10))
plt.xlabel('Xvalues')
plt.ylabel('Yvalues')
plt.scatter(arr,predicted)
plt.show()



     


















    
    















    
    

















    
    






[1.3 1.6 1.9 2.2 2.5]
print out learning rate
0.03
print out the slope
-0.002484249168566121
print step size
-7.452747505698363e-05
[1.30007453 1.60014905 1.90022358 2.20029811 2.50037264]
print out learning rate
0.03
print out the slope
-0.0006232357326142124
print step size
-1.8697071978426373e-05
[1.30009322 1.60018645 1.90027967 2.2003729  2.50046612]
print out learning rate
0.03
print out the slope
-0.00018294549138885192
print step size
-5.4883647416655576e-06
[1.30009871 1.60019743 1.90029614 2.20039485 2.50049356]
print out learning rate
0.03
print out the slope
-6.295271726700503e-05
print step size
-1.888581518010151e-06
[1.3001006  1.6002012  1.9003018  2.20040241 2.50050301]
print out learning rate
0.03
print out the slope
-2.4957830806633716e-05
print step size
-7.487349241990114e-07
[1.30010135 1.6002027  1.90030405 2.2004054  2.50050675]
print out learning rate
0.03
print out the slope
-1.1240042518090735e-05
print step size
-3.3720127554272206e-07
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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[1.30010248 1.60020496 1.90030744 2.20040992 2.5005124 ]
print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
0.03
print out the slope
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print step size
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[1.3001025  1.600205   1.90030749 2.20040999 2.50051249]
print out learning rate
0.03
print out the slope
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print step size
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print out learning rate
0.03
print out the slope
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print step size
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[1.3001025 1.600205  1.9003075 2.20041   2.5005125]
print out learning rate
0.03
print out the slope
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print step size
-1.0954004175112972e-09
[1.3001025  1.600205   1.9003075  2.20041001 2.50051251]
print out learning rate
0.03
print out the slope
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print step size
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print out learning rate
0.03
print out the slope
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print step size
-1.0639248143857235e-09
[1.3001025  1.60020501 1.90030751 2.20041002 2.50051252]
print out learning rate
0.03
print out the slope
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print step size
-1.0487292786374262e-09
[1.3001025  1.60020501 1.90030751 2.20041002 2.50051252]
print out learning rate
0.03
print out the slope
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print step size
-1.0338800724561849e-09
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out the slope
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
0.03
print out the slope
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out the slope
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out the slope
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
0.03
print out the slope
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out the slope
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print step size
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
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print out learning rate
0.03
print out the slope
-1.8141429711621664e-11
print step size
-5.442428913486499e-13
[1.30010268 1.60020535 1.90030803 2.20041071 2.50051338]
print out learning rate
0.03
print out the slope
-1.8138246105681536e-11
print step size
-5.441473831704461e-13
[1.30010268 1.60020535 1.90030803 2.20041071 2.50051338]
print out learning rate
0.03
print out the slope
-1.813506337242474e-11
print step size
-5.440519011727422e-13
[1.30010268 1.60020535 1.90030803 2.20041071 2.50051338]
print out learning rate
0.03
print out the slope
-1.813188151174009e-11
print step size
-5.439564453522027e-13
[1.30010268 1.60020535 1.90030803 2.20041071 2.50051338]
print out learning rate
0.03
print out the slope
-1.8128700523326292e-11
print step size
-5.438610156997887e-13
[1.30010268 1.60020535 1.90030803 2.20041071 2.50051338]
print out learning rate
0.03
print out the slope
-1.8125520406882015e-11
print step size
-5.437656122064604e-13
[1.30010268 1.60020535 1.90030803 2.20041071 2.50051338]
print out learning rate
0.03
print out the slope
-1.812234116185228e-11
print step size
-5.436702348555684e-13
[1.30010268 1.60020535 1.90030803 2.20041071 2.50051338]
print out learning rate
0.03
print out the slope
-1.8119162788126258e-11
print step size
-5.435748836437878e-13
[1.30010268 1.60020535 1.90030803 2.20041071 2.50051338]
print out learning rate
0.03
print out the slope
-1.8115985285339368e-11
print step size
-5.43479558560181e-13
[1.30010268 1.60020535 1.90030803 2.20041071 2.50051338]
print out learning rate
0.03
print out the slope
-1.811280865312719e-11
print step size
-5.433842595938156e-13
[1.30010268 1.60020535 1.90030803 2.20041071 2.50051338]
print out learning rate
0.03
print out the slope
-1.8109632891125332e-11
print step size
-5.4328898673376e-13
[1.30010268 1.60020535 1.90030803 2.20041071 2.50051338]
print out learning rate
0.03
print out the slope
-1.8106457999032903e-11
print step size
-5.431937399709871e-13
[1.30010268 1.60020535 1.90030803 2.20041071 2.50051338]
print out learning rate
0.03
print out the slope
-1.8103283976675914e-11
print step size
-5.430985193002774e-13
[1.30010268 1.60020535 1.90030803 2.20041071 2.50051338]
print out learning rate
0.03
print out the slope
-1.8100110823626702e-11
print step size
-5.43003324708801e-13
[1.30010268 1.60020535 1.90030803 2.20041071 2.50051338]
print out learning rate
0.03
print out the slope
-1.809693853939445e-11
print step size
-5.429081561818334e-13
[1.30010268 1.60020535 1.90030803 2.20041071 2.50051338]
print out learning rate
0.03
print out the slope
-1.8093767123932095e-11
print step size
-5.428130137179629e-13
[1.30010268 1.60020535 1.90030803 2.20041071 2.50051338]
print out learning rate
0.03
print out the slope
-1.809059657674883e-11
print step size
-5.427178973024649e-13












    
    



























In [2]:



     


import pandas as pd
from PIL import Image
from numpy import asarray
import numpy as np
import math
import matplotlib.pyplot as plt
import os




######################################################
i = 0
X_values = [] * 500
while i <= 100: 
      i += 0.2
      X_values.append(i)
      


count = 0
Y_values = [] * 500
while count <= 499:
    Y_values.append(1/X_values[count])
    count += 1

print('\n')
print('\n')
plt.figure(figsize=(20,10))
plt.xlabel('X_values')
plt.ylabel('Y_values')
#plt.plot(X_values, Y_values)
plt.scatter(X_values, Y_values)
plt.show()

count = 0
Test_Xvalues = [] * 100
Test_Yvalues = [] * 100
Training_Xvalues = [] * 400
Training_Yvalues = [] * 400

while count <= 499:
      if count % 5 == 0:
            Test_Xvalues.append(X_values[count])
          
            Test_Yvalues.append(Y_values[count])
      else:
        Training_Xvalues.append(X_values[count])
       
        Training_Yvalues.append(Y_values[count])    
     
      count += 1
        
x1 = len(Test_Xvalues)
x2 = len(Test_Yvalues)
x3 = len(Training_Xvalues)
x4 = len(Training_Yvalues)
x5 = len(X_values)


count = 0
while count < 400:
    Training_Yvalues[count] = Training_Yvalues[count] / 100
    count += 1

count = 0
while count < 100:
    Test_Yvalues[count] = Test_Yvalues[count] / 100
    count += 1

print('\n')
print(x1)
print(x2)
print(x3)
print(x4)
print(x5)

print('\n')
print('\n')
#plt.figure(figsize=(20,10))
plt.xlabel('Training_Xvalues')
plt.ylabel('Training_Yvalues')
plt.scatter(Training_Xvalues, Training_Yvalues)
plt.show()

print('\n')
#plt.figure(figsize=(20,10))
plt.xlabel('Test_Xvalues')
plt.ylabel('Test_Yvalues')
plt.scatter(Test_Xvalues, Test_Yvalues)
plt.show()

#########################################################








#initializing random weights
w1 = 0.3
w2 = 0.4
w3 = 0.5
w4 = 0.2
w5 = 0.7
w6 = 0.6
w7 = 0.3
w8 = 0.1

#initialize the bias to be a constant 1
bias = 1



#this will save the X values at each node
array1 = [0 for i in range(400)] 
array2 = [0 for i in range(400)] 
array3 = [0 for i in range(400)] 
array4 = [0 for i in range(400)] 
array5 = [0 for i in range(400)] 



#this will save the Y values at each node
Node1 = [0 for i in range(400)] 
Node2 = [0 for i in range(400)] 
Node3 = [0 for i in range(400)] 
Node4 = [0 for i in range(400)] 
Node5 = [0 for i in range(400)] 



#intialize the learning rate
learningrate = 0.03


#initialize the actual Y value for the output
observe = Training_Yvalues


#initialize the actual X value for the input
arr = Training_Xvalues


#Graph the X and Y values 
#print('\n')
#plt.xlabel('Xvalues')
#plt.ylabel('Yvalues')
#plt.scatter(arr, observe)
#plt.show()



# Train your data
epoch = 0
while epoch < 10000:
    
    # Get the X-values at each node
    # Weight1 multiplied by the values of the input array
    array1 = w1 * np.array(arr) + bias
    
    # Weight2 multiplied by the vales of the input array
    array2 = w2 * np.array(arr) + bias
    
    #print(array1)
    
    
     # Get the Y-values at each node
    count = 0
    while count < 400:
        # Node 1: takes in the input X and passes it through the sigmoid function then stores it at the corresponding Y
        Node1[count] = 1 / (1 + math.exp(-(array1[count])))
 
        # Node 2: takes in the input X and passes it through the sigmoid function then stores it at the correspodning X
        Node2[count] = 1 / (1 + math.exp(-array2[count]))
        count += 1

    
    
    
    array3 = w3 * np.array(Node1) + w4 * np.array(Node2) + bias

    array4 = w5 * np.array(Node1) + w6 * np.array(Node2) + bias

    count = 0
    while count < 400:
        # Node 1
        Node3[count] = 1 / (1 + math.exp(-array3[count]))
    
        # Node 2
        Node4[count] = 1 / (1 + math.exp(-array4[count]))
        count += 1





    array5 = w7 * np.array(Node3) + w8 * np.array(Node4) + bias
    count = 0
    while count < 400:  
        # Node 5
        Node5[count] = 1 / (1 + math.exp(-array5[count]))
        count += 1
    # The predicted Y values are stores at node 5
    predicted = Node5
    
    

    
    
    

    # intiliazing a storage for each weight, which you will use to obtain the new weight in gradient descent
    w1old = w1
    w2old = w2
    w3old = w3
    w4old = w4
    w5old = w5
    w6old = w6
    w7old = w7
    w8old = w8    
    
    #gradient descent
    count = 0
    while count < 400:
        # Get the derivative for each node using it's correspoding X values
        s = 1/(1 + math.exp(-array1[count]))
        dernode1 = s*(1-s)
    
        s = 1/(1 + math.exp(-array2[count]))
        dernode2 = s*(1-s)
    
        s = 1/(1 + math.exp(-array3[count]))
        dernode3 = s*(1-s)
    
        s = 1/(1 + math.exp(-array4[count]))
        dernode4 = s*(1-s)
    
        s = 1/(1 + math.exp(-array5[count]))
        dernode5 = s*(1-s)
    
    #Adding the derivative for all the indexes using sum of sqaure Residuals
    #For the first iteration, replace all the weights at that index
        if count == 0:
            
            w1 = -2 * (observe[count] - predicted[count]) * arr[count] * dernode1 * w3 * dernode1 * w5 * dernode3 * w7 * dernode4 * w8 * dernode5

            w2 = -2 * (observe[count] - predicted[count]) * arr[count] * w4 * dernode2 * w6 * dernode2 * w7 * dernode3 * w8 * dernode4 * dernode5
    
    
            w3 = -2 * (observe[count] - predicted[count]) * Node1[count] * dernode3 * w7 * dernode5
    
            w4 = -2 * (observe[count] - predicted[count]) * Node2[count] * dernode3 * w7 * dernode5
    
            w5 = -2 * (observe[count] - predicted[count]) * Node1[count] * dernode4 * w8 * dernode5
    
            w6 = -2 * (observe[count] - predicted[count]) * Node2[count] * dernode4 * w8 * dernode5
    
    
            w7 = -2 * (observe[count] - predicted[count]) * Node3[count] * dernode5
    
            w8 = -2 * (observe[count] - predicted[count]) * Node4[count] * dernode5
        
       #For all the other iteration, add the weight to that index to get your final weights 
        else:
            

        
            w1 += -2 * (observe[count] - predicted[count]) * arr[count] * dernode1 * w3 * dernode1 * w5 * dernode3 * w7 * dernode4 * w8 * dernode5      

            w2 += -2 * (observe[count] - predicted[count]) * arr[count] * w4 * dernode2 * w6 * dernode2 * w7 * dernode3 * w8 * dernode4 * dernode5
    
    
            w3 += -2 * (observe[count] - predicted[count]) * Node1[count] * dernode3 * w7 * dernode5
    
            w4 += -2 * (observe[count] - predicted[count]) * Node2[count] * dernode3 * w7 * dernode5
    
            w5 += -2 * (observe[count] - predicted[count]) * Node1[count] * dernode4 * w8 * dernode5
    
            w6 += -2 * (observe[count] - predicted[count]) * Node2[count] * dernode4 * w8 * dernode5
    
    
            w7 += -2 * (observe[count] - predicted[count]) * Node3[count] * dernode5
    
            w8 += -2 * (observe[count] - predicted[count]) * Node4[count] * dernode5
        
        count += 1
    
    #Using gradient descent to find the where the slope equals to zero fast for each weight.

    stepsize = w1 * learningrate
    #print("print out learning rate")
    #print(learningrate)
    
    #print("print out the slope")
    #print(w1)
    
    #print("print step size")
    #print(stepsize)
    
    w1 = w1old - stepsize


    stepsize = w2 * learningrate
    w2 = w2old - stepsize

    stepsize = w3 * learningrate
    w3 = w3old - stepsize

    stepsize = w4 * learningrate
    w4 = w4old - stepsize

    stepsize = w5 * learningrate
    w5 = w5old - stepsize

    stepsize = w6 * learningrate
    w6 = w6old - stepsize

    stepsize = w7 * learningrate
    w7 = w7old - stepsize

    stepsize = w8 * learningrate
    w8 = w8old - stepsize
    
    epoch += 1




####################################################################################
#Using the same inputs to see if your algorithm is correct, then graph at the end using the observe X values with the final
 #predicted Y values

#this will save the X values at each node
array1 = [0 for i in range(100)] 
array2 = [0 for i in range(100)] 
array3 = [0 for i in range(100)] 
array4 = [0 for i in range(100)] 
array5 = [0 for i in range(100)] 



#this will save the Y values at each node
Node1 = [0 for i in range(100)] 
Node2 = [0 for i in range(100)] 
Node3 = [0 for i in range(100)] 
Node4 = [0 for i in range(100)] 
Node5 = [0 for i in range(100)] 


arr = Test_Xvalues
    
# Weight1 multiplied by the values of the input array
array1 = w1 * np.array(arr) + bias
# Weight2 multiplied by the vales of the input array
array2 = w2 * np.array(arr) + bias


count = 0
while count < 100:
    # Node 1
    Node1[count] = 1 / (1 + math.exp(-array1[count]))
    
    # Node 2
    Node2[count] = 1 / (1 + math.exp(-array2[count]))
    count += 1

    
    
    
array3 = w3 * np.array(Node1) + w4 * np.array(Node2) + bias

array4 = w5 * np.array(Node1) + w6 * np.array(Node2) + bias

count = 0
while count < 100:
    # Node 1
    Node3[count] = 1 / (1 + math.exp(-array3[count]))
    
    # Node 2
    Node4[count] = 1 / (1 + math.exp(-array4[count]))
    count += 1





array5 = w7 * np.array(Node3) + w8 * np.array(Node4) + bias
count = 0
while count < 100:  
    # Node 5
    Node5[count] = 1 / (1 + math.exp(-array5[count]))
    count += 1
    
predicted = Node5

print("print the length of prediction")
print(len(predicted))

print("Print the length of X values")
print(len(arr))

print('\n')
#plt.figure(figsize=(20,10))
plt.xlabel('Xvalues')
plt.ylabel('Yvalues')
plt.scatter(arr,predicted)
plt.show()



     


















    
    


















    
    

















    
    






100
100
400
400
500














    
    

















    
    















    
    

















    
    






print the length of prediction
100
Print the length of X values
100












    
    



























In [83]:



     


import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn import datasets
import numpy as np
import sklearn
from sklearn.decomposition import PCA

#open iris dataset
iris = datasets.load_iris()
#print(iris)

#print(len(iris))

#X = iris.data[:, :2]  # we only take the first two features.
#print(X)
#print(len(X))




array = iris.data[:, :4]



#print(len(array))

print(array)







#Setosa
group1 = [[0 for i in range(4)] for j in range(50)]

#Vesicolour
group2 = [[0 for i in range(4)] for j in range(50)]

#Virginia
group3 = [[0 for i in range(4)] for j in range(50)]

#Find the maximum value of each column
col0max = 0
col1max = 0
col2max = 0
col3max = 0
row = 0
col = 0
while row < 150:
    col = 0
    while col < 4:
        if col == 0:
            if col0max < array[row][col]:
                col0max = array[row][col]
        if col == 1:
            if col1max < array[row][col]:
                col1max = array[row][col]
        if col == 2:
            if col2max < array[row][col]:
                col2max = array[row][col]        
        if col == 3:
            if col3max < array[row][col]:
                col3max = array[row][col]
        col += 1
    row += 1

    
print("print maximum value at column 0")
print(col0max)

print("print maximum value at column 1")
print(col1max)




row = 0
col = 0
while row < 150:
    col = 0
    while col < 4:
        if col == 0:
            
            array[row][col] = (array[row][col])/col0max
            
        if col == 1:
            
            array[row][col] = (array[row][col])/col1max
            
        if col == 2:
            
            array[row][col] = (array[row][col])/col2max 
            
        if col == 3:
            
            array[row][col] = (array[row][col])/col3max
            
        col += 1
    row += 1

print("print the normalized matrix")
print(array)



# Seperate all the different classes

row1 = 0
row2 = 0

col = 0
row = 0
while row < 150:
    col = 0
    while col < 4:
        if row < 50:
            group1[row][col] = array[row][col]
        
        elif 50 <= row and row < 100:
            group2[row1][col] = array[row][col]
           
        else:
            group3[row2][col] = array[row][col]
        col += 1
    
    if row < 50:
        row1 = 0
    else:
        row1 += 1
    
    if row < 100:
        row2 = 0
    else:
        row2 += 1
    
    row += 1
    
    
    
#print("print out group1")
#print(group1)
    
#print("print out group2")
#print(group2)

#print("print out group3")
#print(group3)





# The shuffle of group1, Setosa
print("print out again group1")
print(group1)
np.random.shuffle(group1)

#The shuffle of group2, Vesicolour
np.random.shuffle(group2)
np.random.shuffle(group3)
#print(group1)

#The shuffle of group3, Virginia
print('\n')
print("print group 1 shuffle")
print(group1)


#initialize the array of targeted value
targetvalues = [0 for i in range(150)]


#initialize the groups which will be use for training and testing
mixgroup1 = group1
mixgroup2 = group2
mixgroup3 = group3



combine = [[0 for i in range(4)] for j in range(150)]


#combine the mix matrix for each group
col = 0
row = 0
while row < 150:
    col = 0
    while col < 4:
        if row < 50:
            combine[row][col] = group1[row][col]
        
        elif 50 <= row and row < 100:
            combine[row][col] = group2[row1][col]
           
        else:
            combine[row][col] = group3[row2][col]
        col += 1
    
    if row < 50:
        row1 = 0
    else:
        row1 += 1
    
    if row < 100:
        row2 = 0
    else:
        row2 += 1
    
    row += 1

    
print('\n')
print("print combine")
print(combine)  










# The entire matrix is now mix and alternating between the classes
combinemix = [[0 for i in range(4)] for j in range(150)]


row1 = 50
row2 = 100
row3 = 0

count = 0
resetcount = 1
reset = 1
col = 0
row = 0
while row < 150:
    reset = resetcount % 3
    
    if reset == 1:
        targetvalues[count] = 0
        col = 0
        while col < 4:
            combinemix[row][col] = combine[row3][col]
            col += 1
        row3 += 1
    elif reset == 2:
        targetvalues[count] = 1
        col = 0
        while col < 4:
            combinemix[row][col] = combine[row1][col]
            col += 1
        row1 += 1
    else:
        targetvalues[count] = 2
        col = 0
        while col < 4:
            combinemix[row][col] = combine[row2][col]
            col += 1
        row2 += 1
    
    if reset == 0:
        resetcount = 1
    else:
        resetcount += 1
    count += 1
    row += 1

print('\n')
print("print the combine matrix")    
print(combinemix)
    
    

    
target1 = [0 for i in range(50)]
target2 = [0 for i in range(50)]
target3 = [0 for i in range(50)]

    
print("print the values of the target array")
print(targetvalues)


# Seperate the mixgroups respective targeted values
count1 = 0
count2 = 0
count = 0    
while count < 150:
    if count < 50:
        target1[count] = targetvalues[count]
    elif count < 100:
        target2[count1] = targetvalues[count]
        count1 += 1
    else:
        target3[count2] = targetvalues[count]
        count2 += 1
    count += 1
    
print("print target1 values")  
print(target1)

print("print target2 values")
print(target2)

print("print target3 values")
print(target3)    

#normalize the targeted values

count = 0
while count < 50:
    target1[count] = target1[count] / 2
    target2[count] = target2[count] / 2
    target3[count] = target3[count] / 2
    count += 1
# Now save the combine matrix into their respective groups
row1 = 0
row2 = 0

col = 0
row = 0
while row < 150:
    col = 0
    while col < 4:
        if row < 50:
            mixgroup1[row][col] = combinemix[row][col]
        
        elif 50 <= row and row < 100:
            mixgroup2[row1][col] = combinemix[row][col]
           
        else:
            mixgroup3[row2][col] = combinemix[row][col]
        col += 1
    
    if row < 50:
        row1 = 0
    else:
        row1 += 1
    
    if row < 100:
        row2 = 0
    else:
        row2 += 1
    
    row += 1   

print('\n')
print("print out the mix group 1")
print(mixgroup1)


sett = [0 for i in range(50)]

count = 0
while count < 50:
    sett[count] = count
    count += 1

print("print sections testing, which will be later use to compare the predicted values")

print("print group 3, section 1")

plt.xlabel('input')
plt.ylabel('grp 3 pred')
plt.scatter(sett, target3)
plt.show()
     
    
     


print("print group 1, section 2")

plt.xlabel('input')
plt.ylabel('grp 1 pred')
plt.scatter(sett, target1)
plt.show()


print("print group 2, section 3")

plt.xlabel('input')
plt.ylabel('grp 2 pred')
plt.scatter(sett, target2)
plt.show()



#################################################################################

wrong = 0

#initializing random weights
w1 = 0.3
w2 = 0.4
w3 = 0.5
w4 = 0.2
w5 = 0.7
w6 = 0.6
w7 = 0.3
w8 = 0.1
w9 = 0.61
w10 = 0.24
w11 = 0.9
w12 = 0.11
w13 = 0.41
w14 = 0.32

#initialize the bias to be a constant 1
bias = 1

#intitialize the weights of the bias
bw1 = 0.2
bw2 = 0.5
bw3 = 0.7
bw4 = 0.3
bw5 = 0.4



#this will save the X values at each node
array1 = 1
array2 = 1
array3 = 1
array4 = 1
array5 = 1



#this will save the Y values at each node
Node1 = 1
Node2 = 1
Node3 = 1
Node4 = 1
Node5 = 1



#intialize the learning rate
learningrate = 0.01


#initialize the actual Y value for the output
observe = 1

# I know after you have to regularize the data of observe


#initialize the actual X value for the input/ you don't regularize the X input
arr1 = 1
arr2 = 1
arr3 = 1
arr4 = 1

#Graph the X and Y values 
#print('\n')
#plt.xlabel('Xvalues')
#plt.ylabel('Yvalues')
#plt.scatter(inp, ob)
#plt.show()

er= [0 for i in range(50)]

bob= [0 for i in range(50)]

count = 0
while count < 50:
    bob[count] = count
    count += 1

    



index = 0
# Train your data
epoch = 0
while epoch < 200:
    mixgroup1, target1  = sklearn.utils.shuffle(mixgroup1, target1)
    row = 0
    
    while row < 50:
        
        observe = target1[row]
        col = 0
        
        while col < 4:
            
            if col == 0:
                arr1 = mixgroup1[row][col]
            if col == 1:
                arr2 = mixgroup1[row][col]
            if col == 2:
                arr3 = mixgroup1[row][col]
            if col == 3:
                arr4 = mixgroup1[row][col]
                
            col += 1
        

    

        # Weight1 multiplied by the values of the input array
        array1 = w1 * arr1 + w2 * arr2 + w3 * arr3 + w4 * arr4 + bias * bw1
    
        # Weight2 multiplied by the vales of the input array
        array2 = w5 * arr1 + w6 * arr2 + w7 * arr3 + w8 * arr4 + bias * bw2
    
        #print(array1)
    


   
        # Node 1: takes in the input X and passes it through the sigmoid function then stores it at the corresponding Y
        Node1 = 1 / (1 + math.exp(-(array1)))
 
        # Node 2: takes in the input X and passes it through the sigmoid function then stores it at the correspodning X
        Node2 = 1 / (1 + math.exp(-array2))


    
    
    
        array3 = w9 * Node1 + w10 * Node2 + bias * bw3

        array4 = w11 * Node1 + w12 * Node2 + bias * bw4

        count = 0
    
        # function 
        # Node 3 and 4
        Node3 = 1 / (1 + math.exp(-array3))
    
        # Node 2
        Node4 = 1 / (1 + math.exp(-array4))
    





        array5 = w13 * Node3 + w14 * Node4 + bias * bw5
 
        # Node 5
        Node5 = 1 / (1 + math.exp(-array5))
        # The predicted Y values are stores at node 5
        predicted = Node5
    
    
        error = observe - predicted 
    
        #print(error)
   
    

        # intiliazing a storage for each weight, which you will use to obtain the new weight in gradient descent
        w1old = w1
        w2old = w2
        w3old = w3
        w4old = w4
        w5old = w5
        w6old = w6
        w7old = w7
        w8old = w8    
        w9old = w9
        w10old = w10
        w11old = w11
        w12old = w12
        w13old = w13
        w14old = w14
       
    
        oldbw1 = bw1
        oldbw2 = bw2
        oldbw3 = bw3
        oldbw4 = bw4
        oldbw5 = bw5
    
    
    
    
        #gradient descent
    
        # Get the derivative for each node using it's correspoding X values
        s   = 1/(1 + math.exp(-array5))
        dernode5 = s*(1-s)
        L5  = dernode5 * error
        bw5 = learningrate * L5 * error
        w13  = learningrate * L5 * Node3
        w14  = learningrate * L5 * Node4
    
    
        s   = 1/(1 + math.exp(-array3))
        dernode3 = s*(1-s)
        L3  = dernode3 * ( L5 * w13)
        bw3 = learningrate * L3 * bias
        w9  = learningrate * L3 * Node1
        w10  = learningrate * L3 * Node2
    
    
        s   = 1/(1 + math.exp(-array4))
        dernode4 = s*(1-s)
        L4  = dernode4 * (L5 * w14)
        bw4 = learningrate * L4 * bias
        w11  = learningrate * L4 * Node1
        w12  = learningrate * L4 * Node2
    
    
        s   = 1/(1 + math.exp(-array1))
        dernode1 = s*(1-s)
        L1  = dernode1 * (L3 * w9) * (L4 * w11)
        bw1 = learningrate * L1 * bias
        w1  = learningrate * L1 * arr1 
        w2  = learningrate * L1 * arr2 
        w3  = learningrate * L1 * arr3 
        w4  = learningrate * L1 * arr4 
    
        s   = 1/(1 + math.exp(-array2))
        dernode2 = s*(1-s)
        L2  = dernode2 * (L3 * w10) * (L4 * w12)
        bw2 = learningrate * L2 * bias
        w5  = learningrate * L2 * arr1
        w6  = learningrate * L2 * arr2
        w7  = learningrate * L2 * arr3
        w8  = learningrate * L2 * arr4
    
        #Using gradient descent to find the where the slope equals to zero fast for each weight.
        w1 = w1 + w1old
        w2 = w2 + w2old
        w3 = w3 + w3old
        w4 = w4 + w4old
        w5 = w5 + w5old
        w6 = w6 + w6old
        w7 = w7 + w7old
        w8 = w8 + w8old
        w9 = w9 + w9old
        w10 = w10 + w10old
        w11 = w11 + w11old
        w12 = w12 + w12old
        w13 = w13 + w13old
        w14 = w14 + w14old
       
    
    
        bw1 = bw1 + oldbw1
        bw2 = bw2 + oldbw2
        bw3 = bw3 + oldbw3
        bw4 = bw4 + oldbw4
        bw5 = bw5 + oldbw5
        
        row += 1
 
    
   
    epoch += 1

index = 0
# Train your data
epoch = 0
while epoch < 200:
    mixgroup2, target2  = sklearn.utils.shuffle(mixgroup2, target2)
    row = 0

    while row < 50:
        
        observe = target2[row]
        col = 0
        
        while col < 4:
            
            if col == 0:
                arr1 = mixgroup2[row][col]
            if col == 1:
                arr2 = mixgroup2[row][col]
            if col == 2:
                arr3 = mixgroup2[row][col]
            if col == 3:
                arr4 = mixgroup2[row][col]
                
            col += 1
        

    

        # Weight1 multiplied by the values of the input array
        array1 = w1 * arr1 + w2 * arr2 + w3 * arr3 + w4 * arr4 + bias * bw1
    
        # Weight2 multiplied by the vales of the input array
        array2 = w5 * arr1 + w6 * arr2 + w7 * arr3 + w8 * arr4 + bias * bw2
    
        #print(array1)
    


   
        # Node 1: takes in the input X and passes it through the sigmoid function then stores it at the corresponding Y
        Node1 = 1 / (1 + math.exp(-(array1)))
 
        # Node 2: takes in the input X and passes it through the sigmoid function then stores it at the correspodning X
        Node2 = 1 / (1 + math.exp(-array2))


    
    
    
        array3 = w9 * Node1 + w10 * Node2 + bias * bw3

        array4 = w11 * Node1 + w12 * Node2 + bias * bw4

        count = 0
    
        # function 
        # Node 3 and 4
        Node3 = 1 / (1 + math.exp(-array3))
    
        # Node 2
        Node4 = 1 / (1 + math.exp(-array4))
    





        array5 = w13 * Node3 + w14 * Node4 + bias * bw5
 
        # Node 5
        Node5 = 1 / (1 + math.exp(-array5))
        # The predicted Y values are stores at node 5
        predicted = Node5
    
    
        error = observe - predicted 
    
        #print(error)
   
    

        # intiliazing a storage for each weight, which you will use to obtain the new weight in gradient descent
        w1old = w1
        w2old = w2
        w3old = w3
        w4old = w4
        w5old = w5
        w6old = w6
        w7old = w7
        w8old = w8    
        w9old = w9
        w10old = w10
        w11old = w11
        w12old = w12
        w13old = w13
        w14old = w14
       
    
        oldbw1 = bw1
        oldbw2 = bw2
        oldbw3 = bw3
        oldbw4 = bw4
        oldbw5 = bw5
    
    
    
    
        #gradient descent
    
        # Get the derivative for each node using it's correspoding X values
        s   = 1/(1 + math.exp(-array5))
        dernode5 = s*(1-s)
        L5  = dernode5 * error
        bw5 = learningrate * L5 * error
        w13  = learningrate * L5 * Node3
        w14  = learningrate * L5 * Node4
    
    
        s   = 1/(1 + math.exp(-array3))
        dernode3 = s*(1-s)
        L3  = dernode3 * ( L5 * w13)
        bw3 = learningrate * L3 * bias
        w9  = learningrate * L3 * Node1
        w10  = learningrate * L3 * Node2
    
    
        s   = 1/(1 + math.exp(-array4))
        dernode4 = s*(1-s)
        L4  = dernode4 * (L5 * w14)
        bw4 = learningrate * L4 * bias
        w11  = learningrate * L4 * Node1
        w12  = learningrate * L4 * Node2
    
    
        s   = 1/(1 + math.exp(-array1))
        dernode1 = s*(1-s)
        L1  = dernode1 * (L3 * w9) * (L4 * w11)
        bw1 = learningrate * L1 * bias
        w1  = learningrate * L1 * arr1 
        w2  = learningrate * L1 * arr2 
        w3  = learningrate * L1 * arr3 
        w4  = learningrate * L1 * arr4 
    
        s   = 1/(1 + math.exp(-array2))
        dernode2 = s*(1-s)
        L2  = dernode2 * (L3 * w10) * (L4 * w12)
        bw2 = learningrate * L2 * bias
        w5  = learningrate * L2 * arr1
        w6  = learningrate * L2 * arr2
        w7  = learningrate * L2 * arr3
        w8  = learningrate * L2 * arr4
    
        #Using gradient descent to find the where the slope equals to zero fast for each weight.
        w1 = w1 + w1old
        w2 = w2 + w2old
        w3 = w3 + w3old
        w4 = w4 + w4old
        w5 = w5 + w5old
        w6 = w6 + w6old
        w7 = w7 + w7old
        w8 = w8 + w8old
        w9 = w9 + w9old
        w10 = w10 + w10old
        w11 = w11 + w11old
        w12 = w12 + w12old
        w13 = w13 + w13old
        w14 = w14 + w14old
       
    
    
        bw1 = bw1 + oldbw1
        bw2 = bw2 + oldbw2
        bw3 = bw3 + oldbw3
        bw4 = bw4 + oldbw4
        bw5 = bw5 + oldbw5
        
        row += 1
 
    
   
    epoch += 1

print("The error for the first section of testing")
print("The first section of testing use the first and second group for training, and the third group for testing")
row = 0
while row < 50:
        
        observe = target3[row]
        col = 0
        
        while col < 4:
            
            if col == 0:
                arr1 = mixgroup3[row][col]
            if col == 1:
                arr2 = mixgroup3[row][col]
            if col == 2:
                arr3 = mixgroup3[row][col]
            if col == 3:
                arr4 = mixgroup3[row][col]
                
            col += 1
        

    

        # Weight1 multiplied by the values of the input array
        array1 = w1 * arr1 + w2 * arr2 + w3 * arr3 + w4 * arr4 + bias * bw1
    
        # Weight2 multiplied by the vales of the input array
        array2 = w5 * arr1 + w6 * arr2 + w7 * arr3 + w8 * arr4 + bias * bw2
    
        #print(array1)
    


   
        # Node 1: takes in the input X and passes it through the sigmoid function then stores it at the corresponding Y
        Node1 = 1 / (1 + math.exp(-(array1)))
 
        # Node 2: takes in the input X and passes it through the sigmoid function then stores it at the correspodning X
        Node2 = 1 / (1 + math.exp(-array2))


    
    
    
        array3 = w9 * Node1 + w10 * Node2 + bias * bw3

        array4 = w11 * Node1 + w12 * Node2 + bias * bw4

        count = 0
    
        # function 
        # Node 3 and 4
        Node3 = 1 / (1 + math.exp(-array3))
    
        # Node 2
        Node4 = 1 / (1 + math.exp(-array4))
    





        array5 = w13 * Node3 + w14 * Node4 + bias * bw5
 
        # Node 5
        Node5 = 1 / (1 + math.exp(-array5))
        # The predicted Y values are stores at node 5
        predicted = Node5
    
    
        error = observe - predicted 
        
        er[row] = error
        print(error)
        
            
        row += 1

        
plt.xlabel('error')
plt.ylabel('Tested values')
plt.scatter(bob,er)
plt.show()
#plt.scatter(Test_Xvalues, Test_Yvalues)       
        
print("The error for the second section of testing")

print("In this section, will use the second and third for training and use the first group for testing")
index = 0
# Train your data
epoch = 0
while epoch < 200:
    mixgroup2, target2  = sklearn.utils.shuffle(mixgroup2, target2)
    row = 0
    
    while row < 50:
        
        observe = target2[row]
        col = 0
        
        while col < 4:
            
            if col == 0:
                arr1 = mixgroup2[row][col]
            if col == 1:
                arr2 = mixgroup2[row][col]
            if col == 2:
                arr3 = mixgroup2[row][col]
            if col == 3:
                arr4 = mixgroup2[row][col]
                
            col += 1
        

    

        # Weight1 multiplied by the values of the input array
        array1 = w1 * arr1 + w2 * arr2 + w3 * arr3 + w4 * arr4 + bias * bw1
    
        # Weight2 multiplied by the vales of the input array
        array2 = w5 * arr1 + w6 * arr2 + w7 * arr3 + w8 * arr4 + bias * bw2
    
        #print(array1)
    


   
        # Node 1: takes in the input X and passes it through the sigmoid function then stores it at the corresponding Y
        Node1 = 1 / (1 + math.exp(-(array1)))
 
        # Node 2: takes in the input X and passes it through the sigmoid function then stores it at the correspodning X
        Node2 = 1 / (1 + math.exp(-array2))


    
    
    
        array3 = w9 * Node1 + w10 * Node2 + bias * bw3

        array4 = w11 * Node1 + w12 * Node2 + bias * bw4

        count = 0
    
        # function 
        # Node 3 and 4
        Node3 = 1 / (1 + math.exp(-array3))
    
        # Node 2
        Node4 = 1 / (1 + math.exp(-array4))
    





        array5 = w13 * Node3 + w14 * Node4 + bias * bw5
 
        # Node 5
        Node5 = 1 / (1 + math.exp(-array5))
        # The predicted Y values are stores at node 5
        predicted = Node5
    
    
        error = observe - predicted 
    
        #print(error)
   
    

        # intiliazing a storage for each weight, which you will use to obtain the new weight in gradient descent
        w1old = w1
        w2old = w2
        w3old = w3
        w4old = w4
        w5old = w5
        w6old = w6
        w7old = w7
        w8old = w8    
        w9old = w9
        w10old = w10
        w11old = w11
        w12old = w12
        w13old = w13
        w14old = w14
       
    
        oldbw1 = bw1
        oldbw2 = bw2
        oldbw3 = bw3
        oldbw4 = bw4
        oldbw5 = bw5
    
    
    
    
        #gradient descent
    
        # Get the derivative for each node using it's correspoding X values
        s   = 1/(1 + math.exp(-array5))
        dernode5 = s*(1-s)
        L5  = dernode5 * error
        bw5 = learningrate * L5 * error
        w13  = learningrate * L5 * Node3
        w14  = learningrate * L5 * Node4
    
    
        s   = 1/(1 + math.exp(-array3))
        dernode3 = s*(1-s)
        L3  = dernode3 * ( L5 * w13)
        bw3 = learningrate * L3 * bias
        w9  = learningrate * L3 * Node1
        w10  = learningrate * L3 * Node2
    
    
        s   = 1/(1 + math.exp(-array4))
        dernode4 = s*(1-s)
        L4  = dernode4 * (L5 * w14)
        bw4 = learningrate * L4 * bias
        w11  = learningrate * L4 * Node1
        w12  = learningrate * L4 * Node2
    
    
        s   = 1/(1 + math.exp(-array1))
        dernode1 = s*(1-s)
        L1  = dernode1 * (L3 * w9) * (L4 * w11)
        bw1 = learningrate * L1 * bias
        w1  = learningrate * L1 * arr1 
        w2  = learningrate * L1 * arr2 
        w3  = learningrate * L1 * arr3 
        w4  = learningrate * L1 * arr4 
    
        s   = 1/(1 + math.exp(-array2))
        dernode2 = s*(1-s)
        L2  = dernode2 * (L3 * w10) * (L4 * w12)
        bw2 = learningrate * L2 * bias
        w5  = learningrate * L2 * arr1
        w6  = learningrate * L2 * arr2
        w7  = learningrate * L2 * arr3
        w8  = learningrate * L2 * arr4
    
        #Using gradient descent to find the where the slope equals to zero fast for each weight.
        w1 = w1 + w1old
        w2 = w2 + w2old
        w3 = w3 + w3old
        w4 = w4 + w4old
        w5 = w5 + w5old
        w6 = w6 + w6old
        w7 = w7 + w7old
        w8 = w8 + w8old
        w9 = w9 + w9old
        w10 = w10 + w10old
        w11 = w11 + w11old
        w12 = w12 + w12old
        w13 = w13 + w13old
        w14 = w14 + w14old
       
    
    
        bw1 = bw1 + oldbw1
        bw2 = bw2 + oldbw2
        bw3 = bw3 + oldbw3
        bw4 = bw4 + oldbw4
        bw5 = bw5 + oldbw5
        
        row += 1
 
    
   
    epoch += 1
    
    

    
    
    
index = 0
# Train your data
epoch = 0
while epoch < 200:
    mixgroup3, target3  = sklearn.utils.shuffle(mixgroup3, target3)
    row = 0
    
    while row < 50:
        
        observe = target3[row]
        col = 0
        
        while col < 4:
            
            if col == 0:
                arr1 = mixgroup3[row][col]
            if col == 1:
                arr2 = mixgroup3[row][col]
            if col == 2:
                arr3 = mixgroup3[row][col]
            if col == 3:
                arr4 = mixgroup3[row][col]
                
            col += 1
        

    

        # Weight1 multiplied by the values of the input array
        array1 = w1 * arr1 + w2 * arr2 + w3 * arr3 + w4 * arr4 + bias * bw1
    
        # Weight2 multiplied by the vales of the input array
        array2 = w5 * arr1 + w6 * arr2 + w7 * arr3 + w8 * arr4 + bias * bw2
    
        #print(array1)
    


   
        # Node 1: takes in the input X and passes it through the sigmoid function then stores it at the corresponding Y
        Node1 = 1 / (1 + math.exp(-(array1)))
 
        # Node 2: takes in the input X and passes it through the sigmoid function then stores it at the correspodning X
        Node2 = 1 / (1 + math.exp(-array2))


    
    
    
        array3 = w9 * Node1 + w10 * Node2 + bias * bw3

        array4 = w11 * Node1 + w12 * Node2 + bias * bw4

        count = 0
    
        # function 
        # Node 3 and 4
        Node3 = 1 / (1 + math.exp(-array3))
    
        # Node 2
        Node4 = 1 / (1 + math.exp(-array4))
    





        array5 = w13 * Node3 + w14 * Node4 + bias * bw5
 
        # Node 5
        Node5 = 1 / (1 + math.exp(-array5))
        # The predicted Y values are stores at node 5
        predicted = Node5
    
    
        error = observe - predicted 
    
        #print(error)
   
    

        # intiliazing a storage for each weight, which you will use to obtain the new weight in gradient descent
        w1old = w1
        w2old = w2
        w3old = w3
        w4old = w4
        w5old = w5
        w6old = w6
        w7old = w7
        w8old = w8    
        w9old = w9
        w10old = w10
        w11old = w11
        w12old = w12
        w13old = w13
        w14old = w14
       
    
        oldbw1 = bw1
        oldbw2 = bw2
        oldbw3 = bw3
        oldbw4 = bw4
        oldbw5 = bw5
    
    
    
    
        #gradient descent
    
        # Get the derivative for each node using it's correspoding X values
        s   = 1/(1 + math.exp(-array5))
        dernode5 = s*(1-s)
        L5  = dernode5 * error
        bw5 = learningrate * L5 * error
        w13  = learningrate * L5 * Node3
        w14  = learningrate * L5 * Node4
    
    
        s   = 1/(1 + math.exp(-array3))
        dernode3 = s*(1-s)
        L3  = dernode3 * ( L5 * w13)
        bw3 = learningrate * L3 * bias
        w9  = learningrate * L3 * Node1
        w10  = learningrate * L3 * Node2
    
    
        s   = 1/(1 + math.exp(-array4))
        dernode4 = s*(1-s)
        L4  = dernode4 * (L5 * w14)
        bw4 = learningrate * L4 * bias
        w11  = learningrate * L4 * Node1
        w12  = learningrate * L4 * Node2
    
    
        s   = 1/(1 + math.exp(-array1))
        dernode1 = s*(1-s)
        L1  = dernode1 * (L3 * w9) * (L4 * w11)
        bw1 = learningrate * L1 * bias
        w1  = learningrate * L1 * arr1 
        w2  = learningrate * L1 * arr2 
        w3  = learningrate * L1 * arr3 
        w4  = learningrate * L1 * arr4 
    
        s   = 1/(1 + math.exp(-array2))
        dernode2 = s*(1-s)
        L2  = dernode2 * (L3 * w10) * (L4 * w12)
        bw2 = learningrate * L2 * bias
        w5  = learningrate * L2 * arr1
        w6  = learningrate * L2 * arr2
        w7  = learningrate * L2 * arr3
        w8  = learningrate * L2 * arr4
    
        #Using gradient descent to find the where the slope equals to zero fast for each weight.
        w1 = w1 + w1old
        w2 = w2 + w2old
        w3 = w3 + w3old
        w4 = w4 + w4old
        w5 = w5 + w5old
        w6 = w6 + w6old
        w7 = w7 + w7old
        w8 = w8 + w8old
        w9 = w9 + w9old
        w10 = w10 + w10old
        w11 = w11 + w11old
        w12 = w12 + w12old
        w13 = w13 + w13old
        w14 = w14 + w14old
       
    
    
        bw1 = bw1 + oldbw1
        bw2 = bw2 + oldbw2
        bw3 = bw3 + oldbw3
        bw4 = bw4 + oldbw4
        bw5 = bw5 + oldbw5
        
        row += 1
 
    
   
    epoch += 1
        

row = 0
while row < 50:
        
        observe = target1[row]
        col = 0
        
        while col < 4:
            
            if col == 0:
                arr1 = mixgroup1[row][col]
            if col == 1:
                arr2 = mixgroup1[row][col]
            if col == 2:
                arr3 = mixgroup1[row][col]
            if col == 3:
                arr4 = mixgroup1[row][col]
                
            col += 1
        

    

        # Weight1 multiplied by the values of the input array
        array1 = w1 * arr1 + w2 * arr2 + w3 * arr3 + w4 * arr4 + bias * bw1
    
        # Weight2 multiplied by the vales of the input array
        array2 = w5 * arr1 + w6 * arr2 + w7 * arr3 + w8 * arr4 + bias * bw2
    
        #print(array1)
    


   
        # Node 1: takes in the input X and passes it through the sigmoid function then stores it at the corresponding Y
        Node1 = 1 / (1 + math.exp(-(array1)))
 
        # Node 2: takes in the input X and passes it through the sigmoid function then stores it at the correspodning X
        Node2 = 1 / (1 + math.exp(-array2))


    
    
    
        array3 = w9 * Node1 + w10 * Node2 + bias * bw3

        array4 = w11 * Node1 + w12 * Node2 + bias * bw4

        count = 0
    
        # function 
        # Node 3 and 4
        Node3 = 1 / (1 + math.exp(-array3))
    
        # Node 2
        Node4 = 1 / (1 + math.exp(-array4))
    





        array5 = w13 * Node3 + w14 * Node4 + bias * bw5
 
        # Node 5
        Node5 = 1 / (1 + math.exp(-array5))
        # The predicted Y values are stores at node 5
        predicted = Node5
    
    
        error = observe - predicted 
        
        er[row] = error
        print(error)
        
            
        row += 1    

plt.xlabel('error')
plt.ylabel('Tested values')
plt.scatter(bob, er)
plt.show()
#plt.scatter(Test_Xvalues, Test_Yvalues)


print("The third section uses the first and the third group for training and the second group for testing")
print("print the error for the third section")
index = 0
# Train your data
epoch = 0
while epoch < 200:
    mixgroup3, target3  = sklearn.utils.shuffle(mixgroup3, target3)
    row = 0
    
    while row < 50:
        
        observe = target3[row]
        col = 0
        
        while col < 4:
            
            if col == 0:
                arr1 = mixgroup3[row][col]
            if col == 1:
                arr2 = mixgroup3[row][col]
            if col == 2:
                arr3 = mixgroup3[row][col]
            if col == 3:
                arr4 = mixgroup3[row][col]
                
            col += 1
        

    

        # Weight1 multiplied by the values of the input array
        array1 = w1 * arr1 + w2 * arr2 + w3 * arr3 + w4 * arr4 + bias * bw1
    
        # Weight2 multiplied by the vales of the input array
        array2 = w5 * arr1 + w6 * arr2 + w7 * arr3 + w8 * arr4 + bias * bw2
    
        #print(array1)
    


   
        # Node 1: takes in the input X and passes it through the sigmoid function then stores it at the corresponding Y
        Node1 = 1 / (1 + math.exp(-(array1)))
 
        # Node 2: takes in the input X and passes it through the sigmoid function then stores it at the correspodning X
        Node2 = 1 / (1 + math.exp(-array2))


    
    
    
        array3 = w9 * Node1 + w10 * Node2 + bias * bw3

        array4 = w11 * Node1 + w12 * Node2 + bias * bw4

        count = 0
    
        # function 
        # Node 3 and 4
        Node3 = 1 / (1 + math.exp(-array3))
    
        # Node 2
        Node4 = 1 / (1 + math.exp(-array4))
    





        array5 = w13 * Node3 + w14 * Node4 + bias * bw5
 
        # Node 5
        Node5 = 1 / (1 + math.exp(-array5))
        # The predicted Y values are stores at node 5
        predicted = Node5
    
    
        error = observe - predicted 
    
        #print(error)
   
    

        # intiliazing a storage for each weight, which you will use to obtain the new weight in gradient descent
        w1old = w1
        w2old = w2
        w3old = w3
        w4old = w4
        w5old = w5
        w6old = w6
        w7old = w7
        w8old = w8    
        w9old = w9
        w10old = w10
        w11old = w11
        w12old = w12
        w13old = w13
        w14old = w14
       
    
        oldbw1 = bw1
        oldbw2 = bw2
        oldbw3 = bw3
        oldbw4 = bw4
        oldbw5 = bw5
    
    
    
    
        #gradient descent
    
        # Get the derivative for each node using it's correspoding X values
        s   = 1/(1 + math.exp(-array5))
        dernode5 = s*(1-s)
        L5  = dernode5 * error
        bw5 = learningrate * L5 * error
        w13  = learningrate * L5 * Node3
        w14  = learningrate * L5 * Node4
    
    
        s   = 1/(1 + math.exp(-array3))
        dernode3 = s*(1-s)
        L3  = dernode3 * ( L5 * w13)
        bw3 = learningrate * L3 * bias
        w9  = learningrate * L3 * Node1
        w10  = learningrate * L3 * Node2
    
    
        s   = 1/(1 + math.exp(-array4))
        dernode4 = s*(1-s)
        L4  = dernode4 * (L5 * w14)
        bw4 = learningrate * L4 * bias
        w11  = learningrate * L4 * Node1
        w12  = learningrate * L4 * Node2
    
    
        s   = 1/(1 + math.exp(-array1))
        dernode1 = s*(1-s)
        L1  = dernode1 * (L3 * w9) * (L4 * w11)
        bw1 = learningrate * L1 * bias
        w1  = learningrate * L1 * arr1 
        w2  = learningrate * L1 * arr2 
        w3  = learningrate * L1 * arr3 
        w4  = learningrate * L1 * arr4 
    
        s   = 1/(1 + math.exp(-array2))
        dernode2 = s*(1-s)
        L2  = dernode2 * (L3 * w10) * (L4 * w12)
        bw2 = learningrate * L2 * bias
        w5  = learningrate * L2 * arr1
        w6  = learningrate * L2 * arr2
        w7  = learningrate * L2 * arr3
        w8  = learningrate * L2 * arr4
    
        #Using gradient descent to find the where the slope equals to zero fast for each weight.
        w1 = w1 + w1old
        w2 = w2 + w2old
        w3 = w3 + w3old
        w4 = w4 + w4old
        w5 = w5 + w5old
        w6 = w6 + w6old
        w7 = w7 + w7old
        w8 = w8 + w8old
        w9 = w9 + w9old
        w10 = w10 + w10old
        w11 = w11 + w11old
        w12 = w12 + w12old
        w13 = w13 + w13old
        w14 = w14 + w14old
       
    
    
        bw1 = bw1 + oldbw1
        bw2 = bw2 + oldbw2
        bw3 = bw3 + oldbw3
        bw4 = bw4 + oldbw4
        bw5 = bw5 + oldbw5
        
        row += 1
 
    
   
    epoch += 1      
      
index = 0
# Train your data
epoch = 0
while epoch < 200:
    mixgroup1, target1  = sklearn.utils.shuffle(mixgroup1, target1)
    row = 0
    
    while row < 50:
        
        observe = target1[row]
        col = 0
        
        while col < 4:
            
            if col == 0:
                arr1 = mixgroup1[row][col]
            if col == 1:
                arr2 = mixgroup1[row][col]
            if col == 2:
                arr3 = mixgroup1[row][col]
            if col == 3:
                arr4 = mixgroup1[row][col]
                
            col += 1
        

    

        # Weight1 multiplied by the values of the input array
        array1 = w1 * arr1 + w2 * arr2 + w3 * arr3 + w4 * arr4 + bias * bw1
    
        # Weight2 multiplied by the vales of the input array
        array2 = w5 * arr1 + w6 * arr2 + w7 * arr3 + w8 * arr4 + bias * bw2
    
        #print(array1)
    


   
        # Node 1: takes in the input X and passes it through the sigmoid function then stores it at the corresponding Y
        Node1 = 1 / (1 + math.exp(-(array1)))
 
        # Node 2: takes in the input X and passes it through the sigmoid function then stores it at the correspodning X
        Node2 = 1 / (1 + math.exp(-array2))


    
    
    
        array3 = w9 * Node1 + w10 * Node2 + bias * bw3

        array4 = w11 * Node1 + w12 * Node2 + bias * bw4

        count = 0
    
        # function 
        # Node 3 and 4
        Node3 = 1 / (1 + math.exp(-array3))
    
        # Node 2
        Node4 = 1 / (1 + math.exp(-array4))
    





        array5 = w13 * Node3 + w14 * Node4 + bias * bw5
 
        # Node 5
        Node5 = 1 / (1 + math.exp(-array5))
        # The predicted Y values are stores at node 5
        predicted = Node5
    
    
        error = observe - predicted 
    
        #print(error)
   
    

        # intiliazing a storage for each weight, which you will use to obtain the new weight in gradient descent
        w1old = w1
        w2old = w2
        w3old = w3
        w4old = w4
        w5old = w5
        w6old = w6
        w7old = w7
        w8old = w8    
        w9old = w9
        w10old = w10
        w11old = w11
        w12old = w12
        w13old = w13
        w14old = w14
       
    
        oldbw1 = bw1
        oldbw2 = bw2
        oldbw3 = bw3
        oldbw4 = bw4
        oldbw5 = bw5
    
    
    
    
        #gradient descent
    
        # Get the derivative for each node using it's correspoding X values
        s   = 1/(1 + math.exp(-array5))
        dernode5 = s*(1-s)
        L5  = dernode5 * error
        bw5 = learningrate * L5 * error
        w13  = learningrate * L5 * Node3
        w14  = learningrate * L5 * Node4
    
    
        s   = 1/(1 + math.exp(-array3))
        dernode3 = s*(1-s)
        L3  = dernode3 * ( L5 * w13)
        bw3 = learningrate * L3 * bias
        w9  = learningrate * L3 * Node1
        w10  = learningrate * L3 * Node2
    
    
        s   = 1/(1 + math.exp(-array4))
        dernode4 = s*(1-s)
        L4  = dernode4 * (L5 * w14)
        bw4 = learningrate * L4 * bias
        w11  = learningrate * L4 * Node1
        w12  = learningrate * L4 * Node2
    
    
        s   = 1/(1 + math.exp(-array1))
        dernode1 = s*(1-s)
        L1  = dernode1 * (L3 * w9) * (L4 * w11)
        bw1 = learningrate * L1 * bias
        w1  = learningrate * L1 * arr1 
        w2  = learningrate * L1 * arr2 
        w3  = learningrate * L1 * arr3 
        w4  = learningrate * L1 * arr4 
    
        s   = 1/(1 + math.exp(-array2))
        dernode2 = s*(1-s)
        L2  = dernode2 * (L3 * w10) * (L4 * w12)
        bw2 = learningrate * L2 * bias
        w5  = learningrate * L2 * arr1
        w6  = learningrate * L2 * arr2
        w7  = learningrate * L2 * arr3
        w8  = learningrate * L2 * arr4
    
        #Using gradient descent to find the where the slope equals to zero fast for each weight.
        w1 = w1 + w1old
        w2 = w2 + w2old
        w3 = w3 + w3old
        w4 = w4 + w4old
        w5 = w5 + w5old
        w6 = w6 + w6old
        w7 = w7 + w7old
        w8 = w8 + w8old
        w9 = w9 + w9old
        w10 = w10 + w10old
        w11 = w11 + w11old
        w12 = w12 + w12old
        w13 = w13 + w13old
        w14 = w14 + w14old
       
    
    
        bw1 = bw1 + oldbw1
        bw2 = bw2 + oldbw2
        bw3 = bw3 + oldbw3
        bw4 = bw4 + oldbw4
        bw5 = bw5 + oldbw5
        
        row += 1
 
    
   
    epoch += 1      

      

      
row = 0
while row < 50:
        
        observe = target1[row]
        col = 0
        
        while col < 4:
            
            if col == 0:
                arr1 = mixgroup1[row][col]
            if col == 1:
                arr2 = mixgroup1[row][col]
            if col == 2:
                arr3 = mixgroup1[row][col]
            if col == 3:
                arr4 = mixgroup1[row][col]
                
            col += 1
        

    

        # Weight1 multiplied by the values of the input array
        array1 = w1 * arr1 + w2 * arr2 + w3 * arr3 + w4 * arr4 + bias * bw1
    
        # Weight2 multiplied by the vales of the input array
        array2 = w5 * arr1 + w6 * arr2 + w7 * arr3 + w8 * arr4 + bias * bw2
    
        #print(array1)
    


   
        # Node 1: takes in the input X and passes it through the sigmoid function then stores it at the corresponding Y
        Node1 = 1 / (1 + math.exp(-(array1)))
 
        # Node 2: takes in the input X and passes it through the sigmoid function then stores it at the correspodning X
        Node2 = 1 / (1 + math.exp(-array2))


    
    
    
        array3 = w9 * Node1 + w10 * Node2 + bias * bw3

        array4 = w11 * Node1 + w12 * Node2 + bias * bw4

        count = 0
    
        # function 
        # Node 3 and 4
        Node3 = 1 / (1 + math.exp(-array3))
    
        # Node 2
        Node4 = 1 / (1 + math.exp(-array4))
    





        array5 = w13 * Node3 + w14 * Node4 + bias * bw5
 
        # Node 5
        Node5 = 1 / (1 + math.exp(-array5))
        # The predicted Y values are stores at node 5
        predicted = Node5
    
    
        error = observe - predicted 
        
    
        print(error)
        er[row] = error
            
        row += 1    

plt.xlabel('error')
plt.ylabel('Tested values')
plt.scatter(bob, er)
plt.show()
#plt.scatter(Test_Xvalues, Test_Yvalues)            

#array [samples: setosa, vesicolour, and Virginia] [ Sepal length, sepal width, petal length, petal width]
#print(array)

#So should I begin by seperating each sample(row) from the stacks of continous rows
#Before that, how do I find the target values first for each row

#y = iris.target



     


















    
    






[[5.1 3.5 1.4 0.2]
 [4.9 3.  1.4 0.2]
 [4.7 3.2 1.3 0.2]
 [4.6 3.1 1.5 0.2]
 [5.  3.6 1.4 0.2]
 [5.4 3.9 1.7 0.4]
 [4.6 3.4 1.4 0.3]
 [5.  3.4 1.5 0.2]
 [4.4 2.9 1.4 0.2]
 [4.9 3.1 1.5 0.1]
 [5.4 3.7 1.5 0.2]
 [4.8 3.4 1.6 0.2]
 [4.8 3.  1.4 0.1]
 [4.3 3.  1.1 0.1]
 [5.8 4.  1.2 0.2]
 [5.7 4.4 1.5 0.4]
 [5.4 3.9 1.3 0.4]
 [5.1 3.5 1.4 0.3]
 [5.7 3.8 1.7 0.3]
 [5.1 3.8 1.5 0.3]
 [5.4 3.4 1.7 0.2]
 [5.1 3.7 1.5 0.4]
 [4.6 3.6 1.  0.2]
 [5.1 3.3 1.7 0.5]
 [4.8 3.4 1.9 0.2]
 [5.  3.  1.6 0.2]
 [5.  3.4 1.6 0.4]
 [5.2 3.5 1.5 0.2]
 [5.2 3.4 1.4 0.2]
 [4.7 3.2 1.6 0.2]
 [4.8 3.1 1.6 0.2]
 [5.4 3.4 1.5 0.4]
 [5.2 4.1 1.5 0.1]
 [5.5 4.2 1.4 0.2]
 [4.9 3.1 1.5 0.2]
 [5.  3.2 1.2 0.2]
 [5.5 3.5 1.3 0.2]
 [4.9 3.6 1.4 0.1]
 [4.4 3.  1.3 0.2]
 [5.1 3.4 1.5 0.2]
 [5.  3.5 1.3 0.3]
 [4.5 2.3 1.3 0.3]
 [4.4 3.2 1.3 0.2]
 [5.  3.5 1.6 0.6]
 [5.1 3.8 1.9 0.4]
 [4.8 3.  1.4 0.3]
 [5.1 3.8 1.6 0.2]
 [4.6 3.2 1.4 0.2]
 [5.3 3.7 1.5 0.2]
 [5.  3.3 1.4 0.2]
 [7.  3.2 4.7 1.4]
 [6.4 3.2 4.5 1.5]
 [6.9 3.1 4.9 1.5]
 [5.5 2.3 4.  1.3]
 [6.5 2.8 4.6 1.5]
 [5.7 2.8 4.5 1.3]
 [6.3 3.3 4.7 1.6]
 [4.9 2.4 3.3 1. ]
 [6.6 2.9 4.6 1.3]
 [5.2 2.7 3.9 1.4]
 [5.  2.  3.5 1. ]
 [5.9 3.  4.2 1.5]
 [6.  2.2 4.  1. ]
 [6.1 2.9 4.7 1.4]
 [5.6 2.9 3.6 1.3]
 [6.7 3.1 4.4 1.4]
 [5.6 3.  4.5 1.5]
 [5.8 2.7 4.1 1. ]
 [6.2 2.2 4.5 1.5]
 [5.6 2.5 3.9 1.1]
 [5.9 3.2 4.8 1.8]
 [6.1 2.8 4.  1.3]
 [6.3 2.5 4.9 1.5]
 [6.1 2.8 4.7 1.2]
 [6.4 2.9 4.3 1.3]
 [6.6 3.  4.4 1.4]
 [6.8 2.8 4.8 1.4]
 [6.7 3.  5.  1.7]
 [6.  2.9 4.5 1.5]
 [5.7 2.6 3.5 1. ]
 [5.5 2.4 3.8 1.1]
 [5.5 2.4 3.7 1. ]
 [5.8 2.7 3.9 1.2]
 [6.  2.7 5.1 1.6]
 [5.4 3.  4.5 1.5]
 [6.  3.4 4.5 1.6]
 [6.7 3.1 4.7 1.5]
 [6.3 2.3 4.4 1.3]
 [5.6 3.  4.1 1.3]
 [5.5 2.5 4.  1.3]
 [5.5 2.6 4.4 1.2]
 [6.1 3.  4.6 1.4]
 [5.8 2.6 4.  1.2]
 [5.  2.3 3.3 1. ]
 [5.6 2.7 4.2 1.3]
 [5.7 3.  4.2 1.2]
 [5.7 2.9 4.2 1.3]
 [6.2 2.9 4.3 1.3]
 [5.1 2.5 3.  1.1]
 [5.7 2.8 4.1 1.3]
 [6.3 3.3 6.  2.5]
 [5.8 2.7 5.1 1.9]
 [7.1 3.  5.9 2.1]
 [6.3 2.9 5.6 1.8]
 [6.5 3.  5.8 2.2]
 [7.6 3.  6.6 2.1]
 [4.9 2.5 4.5 1.7]
 [7.3 2.9 6.3 1.8]
 [6.7 2.5 5.8 1.8]
 [7.2 3.6 6.1 2.5]
 [6.5 3.2 5.1 2. ]
 [6.4 2.7 5.3 1.9]
 [6.8 3.  5.5 2.1]
 [5.7 2.5 5.  2. ]
 [5.8 2.8 5.1 2.4]
 [6.4 3.2 5.3 2.3]
 [6.5 3.  5.5 1.8]
 [7.7 3.8 6.7 2.2]
 [7.7 2.6 6.9 2.3]
 [6.  2.2 5.  1.5]
 [6.9 3.2 5.7 2.3]
 [5.6 2.8 4.9 2. ]
 [7.7 2.8 6.7 2. ]
 [6.3 2.7 4.9 1.8]
 [6.7 3.3 5.7 2.1]
 [7.2 3.2 6.  1.8]
 [6.2 2.8 4.8 1.8]
 [6.1 3.  4.9 1.8]
 [6.4 2.8 5.6 2.1]
 [7.2 3.  5.8 1.6]
 [7.4 2.8 6.1 1.9]
 [7.9 3.8 6.4 2. ]
 [6.4 2.8 5.6 2.2]
 [6.3 2.8 5.1 1.5]
 [6.1 2.6 5.6 1.4]
 [7.7 3.  6.1 2.3]
 [6.3 3.4 5.6 2.4]
 [6.4 3.1 5.5 1.8]
 [6.  3.  4.8 1.8]
 [6.9 3.1 5.4 2.1]
 [6.7 3.1 5.6 2.4]
 [6.9 3.1 5.1 2.3]
 [5.8 2.7 5.1 1.9]
 [6.8 3.2 5.9 2.3]
 [6.7 3.3 5.7 2.5]
 [6.7 3.  5.2 2.3]
 [6.3 2.5 5.  1.9]
 [6.5 3.  5.2 2. ]
 [6.2 3.4 5.4 2.3]
 [5.9 3.  5.1 1.8]]
print maximum value at column 0
7.9
print maximum value at column 1
4.4
print the normalized matrix
[[0.64556962 0.79545455 0.20289855 0.08      ]
 [0.62025316 0.68181818 0.20289855 0.08      ]
 [0.59493671 0.72727273 0.1884058  0.08      ]
 [0.58227848 0.70454545 0.2173913  0.08      ]
 [0.63291139 0.81818182 0.20289855 0.08      ]
 [0.6835443  0.88636364 0.24637681 0.16      ]
 [0.58227848 0.77272727 0.20289855 0.12      ]
 [0.63291139 0.77272727 0.2173913  0.08      ]
 [0.55696203 0.65909091 0.20289855 0.08      ]
 [0.62025316 0.70454545 0.2173913  0.04      ]
 [0.6835443  0.84090909 0.2173913  0.08      ]
 [0.60759494 0.77272727 0.23188406 0.08      ]
 [0.60759494 0.68181818 0.20289855 0.04      ]
 [0.5443038  0.68181818 0.15942029 0.04      ]
 [0.73417722 0.90909091 0.17391304 0.08      ]
 [0.72151899 1.         0.2173913  0.16      ]
 [0.6835443  0.88636364 0.1884058  0.16      ]
 [0.64556962 0.79545455 0.20289855 0.12      ]
 [0.72151899 0.86363636 0.24637681 0.12      ]
 [0.64556962 0.86363636 0.2173913  0.12      ]
 [0.6835443  0.77272727 0.24637681 0.08      ]
 [0.64556962 0.84090909 0.2173913  0.16      ]
 [0.58227848 0.81818182 0.14492754 0.08      ]
 [0.64556962 0.75       0.24637681 0.2       ]
 [0.60759494 0.77272727 0.27536232 0.08      ]
 [0.63291139 0.68181818 0.23188406 0.08      ]
 [0.63291139 0.77272727 0.23188406 0.16      ]
 [0.65822785 0.79545455 0.2173913  0.08      ]
 [0.65822785 0.77272727 0.20289855 0.08      ]
 [0.59493671 0.72727273 0.23188406 0.08      ]
 [0.60759494 0.70454545 0.23188406 0.08      ]
 [0.6835443  0.77272727 0.2173913  0.16      ]
 [0.65822785 0.93181818 0.2173913  0.04      ]
 [0.69620253 0.95454545 0.20289855 0.08      ]
 [0.62025316 0.70454545 0.2173913  0.08      ]
 [0.63291139 0.72727273 0.17391304 0.08      ]
 [0.69620253 0.79545455 0.1884058  0.08      ]
 [0.62025316 0.81818182 0.20289855 0.04      ]
 [0.55696203 0.68181818 0.1884058  0.08      ]
 [0.64556962 0.77272727 0.2173913  0.08      ]
 [0.63291139 0.79545455 0.1884058  0.12      ]
 [0.56962025 0.52272727 0.1884058  0.12      ]
 [0.55696203 0.72727273 0.1884058  0.08      ]
 [0.63291139 0.79545455 0.23188406 0.24      ]
 [0.64556962 0.86363636 0.27536232 0.16      ]
 [0.60759494 0.68181818 0.20289855 0.12      ]
 [0.64556962 0.86363636 0.23188406 0.08      ]
 [0.58227848 0.72727273 0.20289855 0.08      ]
 [0.67088608 0.84090909 0.2173913  0.08      ]
 [0.63291139 0.75       0.20289855 0.08      ]
 [0.88607595 0.72727273 0.68115942 0.56      ]
 [0.81012658 0.72727273 0.65217391 0.6       ]
 [0.87341772 0.70454545 0.71014493 0.6       ]
 [0.69620253 0.52272727 0.57971014 0.52      ]
 [0.82278481 0.63636364 0.66666667 0.6       ]
 [0.72151899 0.63636364 0.65217391 0.52      ]
 [0.79746835 0.75       0.68115942 0.64      ]
 [0.62025316 0.54545455 0.47826087 0.4       ]
 [0.83544304 0.65909091 0.66666667 0.52      ]
 [0.65822785 0.61363636 0.56521739 0.56      ]
 [0.63291139 0.45454545 0.50724638 0.4       ]
 [0.74683544 0.68181818 0.60869565 0.6       ]
 [0.75949367 0.5        0.57971014 0.4       ]
 [0.7721519  0.65909091 0.68115942 0.56      ]
 [0.70886076 0.65909091 0.52173913 0.52      ]
 [0.84810127 0.70454545 0.63768116 0.56      ]
 [0.70886076 0.68181818 0.65217391 0.6       ]
 [0.73417722 0.61363636 0.5942029  0.4       ]
 [0.78481013 0.5        0.65217391 0.6       ]
 [0.70886076 0.56818182 0.56521739 0.44      ]
 [0.74683544 0.72727273 0.69565217 0.72      ]
 [0.7721519  0.63636364 0.57971014 0.52      ]
 [0.79746835 0.56818182 0.71014493 0.6       ]
 [0.7721519  0.63636364 0.68115942 0.48      ]
 [0.81012658 0.65909091 0.62318841 0.52      ]
 [0.83544304 0.68181818 0.63768116 0.56      ]
 [0.86075949 0.63636364 0.69565217 0.56      ]
 [0.84810127 0.68181818 0.72463768 0.68      ]
 [0.75949367 0.65909091 0.65217391 0.6       ]
 [0.72151899 0.59090909 0.50724638 0.4       ]
 [0.69620253 0.54545455 0.55072464 0.44      ]
 [0.69620253 0.54545455 0.53623188 0.4       ]
 [0.73417722 0.61363636 0.56521739 0.48      ]
 [0.75949367 0.61363636 0.73913043 0.64      ]
 [0.6835443  0.68181818 0.65217391 0.6       ]
 [0.75949367 0.77272727 0.65217391 0.64      ]
 [0.84810127 0.70454545 0.68115942 0.6       ]
 [0.79746835 0.52272727 0.63768116 0.52      ]
 [0.70886076 0.68181818 0.5942029  0.52      ]
 [0.69620253 0.56818182 0.57971014 0.52      ]
 [0.69620253 0.59090909 0.63768116 0.48      ]
 [0.7721519  0.68181818 0.66666667 0.56      ]
 [0.73417722 0.59090909 0.57971014 0.48      ]
 [0.63291139 0.52272727 0.47826087 0.4       ]
 [0.70886076 0.61363636 0.60869565 0.52      ]
 [0.72151899 0.68181818 0.60869565 0.48      ]
 [0.72151899 0.65909091 0.60869565 0.52      ]
 [0.78481013 0.65909091 0.62318841 0.52      ]
 [0.64556962 0.56818182 0.43478261 0.44      ]
 [0.72151899 0.63636364 0.5942029  0.52      ]
 [0.79746835 0.75       0.86956522 1.        ]
 [0.73417722 0.61363636 0.73913043 0.76      ]
 [0.89873418 0.68181818 0.85507246 0.84      ]
 [0.79746835 0.65909091 0.8115942  0.72      ]
 [0.82278481 0.68181818 0.84057971 0.88      ]
 [0.96202532 0.68181818 0.95652174 0.84      ]
 [0.62025316 0.56818182 0.65217391 0.68      ]
 [0.92405063 0.65909091 0.91304348 0.72      ]
 [0.84810127 0.56818182 0.84057971 0.72      ]
 [0.91139241 0.81818182 0.88405797 1.        ]
 [0.82278481 0.72727273 0.73913043 0.8       ]
 [0.81012658 0.61363636 0.76811594 0.76      ]
 [0.86075949 0.68181818 0.79710145 0.84      ]
 [0.72151899 0.56818182 0.72463768 0.8       ]
 [0.73417722 0.63636364 0.73913043 0.96      ]
 [0.81012658 0.72727273 0.76811594 0.92      ]
 [0.82278481 0.68181818 0.79710145 0.72      ]
 [0.97468354 0.86363636 0.97101449 0.88      ]
 [0.97468354 0.59090909 1.         0.92      ]
 [0.75949367 0.5        0.72463768 0.6       ]
 [0.87341772 0.72727273 0.82608696 0.92      ]
 [0.70886076 0.63636364 0.71014493 0.8       ]
 [0.97468354 0.63636364 0.97101449 0.8       ]
 [0.79746835 0.61363636 0.71014493 0.72      ]
 [0.84810127 0.75       0.82608696 0.84      ]
 [0.91139241 0.72727273 0.86956522 0.72      ]
 [0.78481013 0.63636364 0.69565217 0.72      ]
 [0.7721519  0.68181818 0.71014493 0.72      ]
 [0.81012658 0.63636364 0.8115942  0.84      ]
 [0.91139241 0.68181818 0.84057971 0.64      ]
 [0.93670886 0.63636364 0.88405797 0.76      ]
 [1.         0.86363636 0.92753623 0.8       ]
 [0.81012658 0.63636364 0.8115942  0.88      ]
 [0.79746835 0.63636364 0.73913043 0.6       ]
 [0.7721519  0.59090909 0.8115942  0.56      ]
 [0.97468354 0.68181818 0.88405797 0.92      ]
 [0.79746835 0.77272727 0.8115942  0.96      ]
 [0.81012658 0.70454545 0.79710145 0.72      ]
 [0.75949367 0.68181818 0.69565217 0.72      ]
 [0.87341772 0.70454545 0.7826087  0.84      ]
 [0.84810127 0.70454545 0.8115942  0.96      ]
 [0.87341772 0.70454545 0.73913043 0.92      ]
 [0.73417722 0.61363636 0.73913043 0.76      ]
 [0.86075949 0.72727273 0.85507246 0.92      ]
 [0.84810127 0.75       0.82608696 1.        ]
 [0.84810127 0.68181818 0.75362319 0.92      ]
 [0.79746835 0.56818182 0.72463768 0.76      ]
 [0.82278481 0.68181818 0.75362319 0.8       ]
 [0.78481013 0.77272727 0.7826087  0.92      ]
 [0.74683544 0.68181818 0.73913043 0.72      ]]
print out again group1
[[0.6455696202531644, 0.7954545454545454, 0.20289855072463767, 0.08], [0.620253164556962, 0.6818181818181818, 0.20289855072463767, 0.08], [0.5949367088607594, 0.7272727272727273, 0.18840579710144928, 0.08], [0.5822784810126581, 0.7045454545454545, 0.21739130434782608, 0.08], [0.6329113924050632, 0.8181818181818181, 0.20289855072463767, 0.08], [0.6835443037974683, 0.8863636363636362, 0.24637681159420288, 0.16], [0.5822784810126581, 0.7727272727272726, 0.20289855072463767, 0.12], [0.6329113924050632, 0.7727272727272726, 0.21739130434782608, 0.08], [0.5569620253164557, 0.6590909090909091, 0.20289855072463767, 0.08], [0.620253164556962, 0.7045454545454545, 0.21739130434782608, 0.04], [0.6835443037974683, 0.8409090909090908, 0.21739130434782608, 0.08], [0.6075949367088607, 0.7727272727272726, 0.2318840579710145, 0.08], [0.6075949367088607, 0.6818181818181818, 0.20289855072463767, 0.04], [0.5443037974683543, 0.6818181818181818, 0.15942028985507248, 0.04], [0.7341772151898733, 0.9090909090909091, 0.17391304347826086, 0.08], [0.7215189873417721, 1.0, 0.21739130434782608, 0.16], [0.6835443037974683, 0.8863636363636362, 0.18840579710144928, 0.16], [0.6455696202531644, 0.7954545454545454, 0.20289855072463767, 0.12], [0.7215189873417721, 0.8636363636363635, 0.24637681159420288, 0.12], [0.6455696202531644, 0.8636363636363635, 0.21739130434782608, 0.12], [0.6835443037974683, 0.7727272727272726, 0.24637681159420288, 0.08], [0.6455696202531644, 0.8409090909090908, 0.21739130434782608, 0.16], [0.5822784810126581, 0.8181818181818181, 0.14492753623188406, 0.08], [0.6455696202531644, 0.7499999999999999, 0.24637681159420288, 0.2], [0.6075949367088607, 0.7727272727272726, 0.27536231884057966, 0.08], [0.6329113924050632, 0.6818181818181818, 0.2318840579710145, 0.08], [0.6329113924050632, 0.7727272727272726, 0.2318840579710145, 0.16], [0.6582278481012658, 0.7954545454545454, 0.21739130434782608, 0.08], [0.6582278481012658, 0.7727272727272726, 0.20289855072463767, 0.08], [0.5949367088607594, 0.7272727272727273, 0.2318840579710145, 0.08], [0.6075949367088607, 0.7045454545454545, 0.2318840579710145, 0.08], [0.6835443037974683, 0.7727272727272726, 0.21739130434782608, 0.16], [0.6582278481012658, 0.9318181818181817, 0.21739130434782608, 0.04], [0.6962025316455696, 0.9545454545454545, 0.20289855072463767, 0.08], [0.620253164556962, 0.7045454545454545, 0.21739130434782608, 0.08], [0.6329113924050632, 0.7272727272727273, 0.17391304347826086, 0.08], [0.6962025316455696, 0.7954545454545454, 0.18840579710144928, 0.08], [0.620253164556962, 0.8181818181818181, 0.20289855072463767, 0.04], [0.5569620253164557, 0.6818181818181818, 0.18840579710144928, 0.08], [0.6455696202531644, 0.7727272727272726, 0.21739130434782608, 0.08], [0.6329113924050632, 0.7954545454545454, 0.18840579710144928, 0.12], [0.5696202531645569, 0.5227272727272726, 0.18840579710144928, 0.12], [0.5569620253164557, 0.7272727272727273, 0.18840579710144928, 0.08], [0.6329113924050632, 0.7954545454545454, 0.2318840579710145, 0.24], [0.6455696202531644, 0.8636363636363635, 0.27536231884057966, 0.16], [0.6075949367088607, 0.6818181818181818, 0.20289855072463767, 0.12], [0.6455696202531644, 0.8636363636363635, 0.2318840579710145, 0.08], [0.5822784810126581, 0.7272727272727273, 0.20289855072463767, 0.08], [0.670886075949367, 0.8409090909090908, 0.21739130434782608, 0.08], [0.6329113924050632, 0.7499999999999999, 0.20289855072463767, 0.08]]


print group 1 shuffle
[[0.6455696202531644, 0.7727272727272726, 0.21739130434782608, 0.08], [0.6835443037974683, 0.7727272727272726, 0.24637681159420288, 0.08], [0.6075949367088607, 0.6818181818181818, 0.20289855072463767, 0.12], [0.6329113924050632, 0.6818181818181818, 0.2318840579710145, 0.08], [0.6329113924050632, 0.7954545454545454, 0.2318840579710145, 0.24], [0.5696202531645569, 0.5227272727272726, 0.18840579710144928, 0.12], [0.6329113924050632, 0.8181818181818181, 0.20289855072463767, 0.08], [0.670886075949367, 0.8409090909090908, 0.21739130434782608, 0.08], [0.6075949367088607, 0.7045454545454545, 0.2318840579710145, 0.08], [0.5822784810126581, 0.7045454545454545, 0.21739130434782608, 0.08], [0.620253164556962, 0.8181818181818181, 0.20289855072463767, 0.04], [0.5949367088607594, 0.7272727272727273, 0.2318840579710145, 0.08], [0.7215189873417721, 0.8636363636363635, 0.24637681159420288, 0.12], [0.6962025316455696, 0.9545454545454545, 0.20289855072463767, 0.08], [0.620253164556962, 0.7045454545454545, 0.21739130434782608, 0.04], [0.620253164556962, 0.7045454545454545, 0.21739130434782608, 0.08], [0.6835443037974683, 0.8863636363636362, 0.18840579710144928, 0.16], [0.6329113924050632, 0.7954545454545454, 0.18840579710144928, 0.12], [0.6455696202531644, 0.8636363636363635, 0.21739130434782608, 0.12], [0.6075949367088607, 0.7727272727272726, 0.27536231884057966, 0.08], [0.5822784810126581, 0.7727272727272726, 0.20289855072463767, 0.12], [0.5569620253164557, 0.6818181818181818, 0.18840579710144928, 0.08], [0.6329113924050632, 0.7499999999999999, 0.20289855072463767, 0.08], [0.5949367088607594, 0.7272727272727273, 0.18840579710144928, 0.08], [0.6835443037974683, 0.8409090909090908, 0.21739130434782608, 0.08], [0.5569620253164557, 0.7272727272727273, 0.18840579710144928, 0.08], [0.6582278481012658, 0.9318181818181817, 0.21739130434782608, 0.04], [0.6582278481012658, 0.7727272727272726, 0.20289855072463767, 0.08], [0.6329113924050632, 0.7727272727272726, 0.21739130434782608, 0.08], [0.6455696202531644, 0.7954545454545454, 0.20289855072463767, 0.08], [0.6075949367088607, 0.7727272727272726, 0.2318840579710145, 0.08], [0.6329113924050632, 0.7272727272727273, 0.17391304347826086, 0.08], [0.6075949367088607, 0.6818181818181818, 0.20289855072463767, 0.04], [0.620253164556962, 0.6818181818181818, 0.20289855072463767, 0.08], [0.6835443037974683, 0.7727272727272726, 0.21739130434782608, 0.16], [0.6835443037974683, 0.8863636363636362, 0.24637681159420288, 0.16], [0.5822784810126581, 0.7272727272727273, 0.20289855072463767, 0.08], [0.6962025316455696, 0.7954545454545454, 0.18840579710144928, 0.08], [0.7341772151898733, 0.9090909090909091, 0.17391304347826086, 0.08], [0.6582278481012658, 0.7954545454545454, 0.21739130434782608, 0.08], [0.7215189873417721, 1.0, 0.21739130434782608, 0.16], [0.6455696202531644, 0.7954545454545454, 0.20289855072463767, 0.12], [0.5443037974683543, 0.6818181818181818, 0.15942028985507248, 0.04], [0.5822784810126581, 0.8181818181818181, 0.14492753623188406, 0.08], [0.6455696202531644, 0.7499999999999999, 0.24637681159420288, 0.2], [0.6329113924050632, 0.7727272727272726, 0.2318840579710145, 0.16], [0.6455696202531644, 0.8409090909090908, 0.21739130434782608, 0.16], [0.6455696202531644, 0.8636363636363635, 0.27536231884057966, 0.16], [0.6455696202531644, 0.8636363636363635, 0.2318840579710145, 0.08], [0.5569620253164557, 0.6590909090909091, 0.20289855072463767, 0.08]]


print combine
[[0.6455696202531644, 0.7727272727272726, 0.21739130434782608, 0.08], [0.6835443037974683, 0.7727272727272726, 0.24637681159420288, 0.08], [0.6075949367088607, 0.6818181818181818, 0.20289855072463767, 0.12], [0.6329113924050632, 0.6818181818181818, 0.2318840579710145, 0.08], [0.6329113924050632, 0.7954545454545454, 0.2318840579710145, 0.24], [0.5696202531645569, 0.5227272727272726, 0.18840579710144928, 0.12], [0.6329113924050632, 0.8181818181818181, 0.20289855072463767, 0.08], [0.670886075949367, 0.8409090909090908, 0.21739130434782608, 0.08], [0.6075949367088607, 0.7045454545454545, 0.2318840579710145, 0.08], [0.5822784810126581, 0.7045454545454545, 0.21739130434782608, 0.08], [0.620253164556962, 0.8181818181818181, 0.20289855072463767, 0.04], [0.5949367088607594, 0.7272727272727273, 0.2318840579710145, 0.08], [0.7215189873417721, 0.8636363636363635, 0.24637681159420288, 0.12], [0.6962025316455696, 0.9545454545454545, 0.20289855072463767, 0.08], [0.620253164556962, 0.7045454545454545, 0.21739130434782608, 0.04], [0.620253164556962, 0.7045454545454545, 0.21739130434782608, 0.08], [0.6835443037974683, 0.8863636363636362, 0.18840579710144928, 0.16], [0.6329113924050632, 0.7954545454545454, 0.18840579710144928, 0.12], [0.6455696202531644, 0.8636363636363635, 0.21739130434782608, 0.12], [0.6075949367088607, 0.7727272727272726, 0.27536231884057966, 0.08], [0.5822784810126581, 0.7727272727272726, 0.20289855072463767, 0.12], [0.5569620253164557, 0.6818181818181818, 0.18840579710144928, 0.08], [0.6329113924050632, 0.7499999999999999, 0.20289855072463767, 0.08], [0.5949367088607594, 0.7272727272727273, 0.18840579710144928, 0.08], [0.6835443037974683, 0.8409090909090908, 0.21739130434782608, 0.08], [0.5569620253164557, 0.7272727272727273, 0.18840579710144928, 0.08], [0.6582278481012658, 0.9318181818181817, 0.21739130434782608, 0.04], [0.6582278481012658, 0.7727272727272726, 0.20289855072463767, 0.08], [0.6329113924050632, 0.7727272727272726, 0.21739130434782608, 0.08], [0.6455696202531644, 0.7954545454545454, 0.20289855072463767, 0.08], [0.6075949367088607, 0.7727272727272726, 0.2318840579710145, 0.08], [0.6329113924050632, 0.7272727272727273, 0.17391304347826086, 0.08], [0.6075949367088607, 0.6818181818181818, 0.20289855072463767, 0.04], [0.620253164556962, 0.6818181818181818, 0.20289855072463767, 0.08], [0.6835443037974683, 0.7727272727272726, 0.21739130434782608, 0.16], [0.6835443037974683, 0.8863636363636362, 0.24637681159420288, 0.16], [0.5822784810126581, 0.7272727272727273, 0.20289855072463767, 0.08], [0.6962025316455696, 0.7954545454545454, 0.18840579710144928, 0.08], [0.7341772151898733, 0.9090909090909091, 0.17391304347826086, 0.08], [0.6582278481012658, 0.7954545454545454, 0.21739130434782608, 0.08], [0.7215189873417721, 1.0, 0.21739130434782608, 0.16], [0.6455696202531644, 0.7954545454545454, 0.20289855072463767, 0.12], [0.5443037974683543, 0.6818181818181818, 0.15942028985507248, 0.04], [0.5822784810126581, 0.8181818181818181, 0.14492753623188406, 0.08], [0.6455696202531644, 0.7499999999999999, 0.24637681159420288, 0.2], [0.6329113924050632, 0.7727272727272726, 0.2318840579710145, 0.16], [0.6455696202531644, 0.8409090909090908, 0.21739130434782608, 0.16], [0.6455696202531644, 0.8636363636363635, 0.27536231884057966, 0.16], [0.6455696202531644, 0.8636363636363635, 0.2318840579710145, 0.08], [0.5569620253164557, 0.6590909090909091, 0.20289855072463767, 0.08], [0.6962025316455696, 0.5454545454545454, 0.5507246376811593, 0.44000000000000006], [0.7468354430379747, 0.7272727272727273, 0.6956521739130435, 0.72], [0.6835443037974683, 0.6818181818181818, 0.6521739130434783, 0.6], [0.7341772151898733, 0.5909090909090908, 0.5797101449275363, 0.48], [0.7088607594936708, 0.6818181818181818, 0.5942028985507245, 0.52], [0.7215189873417721, 0.6590909090909091, 0.6086956521739131, 0.52], [0.7088607594936708, 0.6136363636363636, 0.6086956521739131, 0.52], [0.6962025316455696, 0.5909090909090908, 0.6376811594202899, 0.48], [0.8227848101265822, 0.6363636363636362, 0.6666666666666666, 0.6], [0.7088607594936708, 0.6818181818181818, 0.6521739130434783, 0.6], [0.7088607594936708, 0.5681818181818181, 0.5652173913043478, 0.44000000000000006], [0.7848101265822784, 0.6590909090909091, 0.6231884057971014, 0.52], [0.6582278481012658, 0.6136363636363636, 0.5652173913043478, 0.5599999999999999], [0.6455696202531644, 0.5681818181818181, 0.43478260869565216, 0.44000000000000006], [0.7974683544303797, 0.5227272727272726, 0.6376811594202899, 0.52], [0.7721518987341771, 0.6363636363636362, 0.6811594202898551, 0.48], [0.8481012658227848, 0.6818181818181818, 0.7246376811594203, 0.6799999999999999], [0.860759493670886, 0.6363636363636362, 0.6956521739130435, 0.5599999999999999], [0.8860759493670886, 0.7272727272727273, 0.6811594202898551, 0.5599999999999999], [0.6329113924050632, 0.45454545454545453, 0.5072463768115941, 0.4], [0.7215189873417721, 0.5909090909090908, 0.5072463768115941, 0.4], [0.7974683544303797, 0.5681818181818181, 0.7101449275362319, 0.6], [0.7594936708860759, 0.7727272727272726, 0.6521739130434783, 0.64], [0.7088607594936708, 0.6590909090909091, 0.5217391304347826, 0.52], [0.7721518987341771, 0.6363636363636362, 0.5797101449275363, 0.52], [0.8354430379746834, 0.6818181818181818, 0.6376811594202899, 0.5599999999999999], [0.6329113924050632, 0.5227272727272726, 0.47826086956521735, 0.4], [0.7848101265822784, 0.5, 0.6521739130434783, 0.6], [0.7721518987341771, 0.6590909090909091, 0.6811594202898551, 0.5599999999999999], [0.7594936708860759, 0.5, 0.5797101449275363, 0.4], [0.7341772151898733, 0.6136363636363636, 0.5942028985507245, 0.4], [0.8481012658227848, 0.7045454545454545, 0.6811594202898551, 0.6], [0.7215189873417721, 0.6818181818181818, 0.6086956521739131, 0.48], [0.7721518987341771, 0.6818181818181818, 0.6666666666666666, 0.5599999999999999], [0.7215189873417721, 0.6363636363636362, 0.5942028985507245, 0.52], [0.810126582278481, 0.7272727272727273, 0.6521739130434783, 0.6], [0.8481012658227848, 0.7045454545454545, 0.6376811594202899, 0.5599999999999999], [0.7594936708860759, 0.6590909090909091, 0.6521739130434783, 0.6], [0.7974683544303797, 0.7499999999999999, 0.6811594202898551, 0.64], [0.8734177215189873, 0.7045454545454545, 0.7101449275362319, 0.6], [0.810126582278481, 0.6590909090909091, 0.6231884057971014, 0.52], [0.620253164556962, 0.5454545454545454, 0.47826086956521735, 0.4], [0.6962025316455696, 0.5227272727272726, 0.5797101449275363, 0.52], [0.6962025316455696, 0.5681818181818181, 0.5797101449275363, 0.52], [0.6962025316455696, 0.5454545454545454, 0.5362318840579711, 0.4], [0.8354430379746834, 0.6590909090909091, 0.6666666666666666, 0.52], [0.7341772151898733, 0.6136363636363636, 0.5652173913043478, 0.48], [0.7215189873417721, 0.6363636363636362, 0.6521739130434783, 0.52], [0.7594936708860759, 0.6136363636363636, 0.7391304347826086, 0.64], [0.7468354430379747, 0.6818181818181818, 0.6086956521739131, 0.6], [0.810126582278481, 0.6363636363636362, 0.8115942028985507, 0.8400000000000001], [0.860759493670886, 0.7272727272727273, 0.855072463768116, 0.9199999999999999], [0.7341772151898733, 0.6363636363636362, 0.7391304347826086, 0.96], [0.9746835443037974, 0.5909090909090908, 1.0, 0.9199999999999999], [0.8987341772151898, 0.6818181818181818, 0.855072463768116, 0.8400000000000001], [0.9367088607594937, 0.6363636363636362, 0.8840579710144927, 0.76], [0.7974683544303797, 0.6136363636363636, 0.7101449275362319, 0.72], [0.7848101265822784, 0.6363636363636362, 0.6956521739130435, 0.72], [0.810126582278481, 0.7045454545454545, 0.7971014492753623, 0.72], [0.9113924050632911, 0.6818181818181818, 0.8405797101449275, 0.64], [0.7974683544303797, 0.6363636363636362, 0.7391304347826086, 0.6], [1.0, 0.8636363636363635, 0.927536231884058, 0.8], [0.810126582278481, 0.6363636363636362, 0.8115942028985507, 0.8800000000000001], [0.7594936708860759, 0.5, 0.7246376811594203, 0.6], [0.7974683544303797, 0.5681818181818181, 0.7246376811594203, 0.76], [0.8734177215189873, 0.7045454545454545, 0.782608695652174, 0.8400000000000001], [0.7974683544303797, 0.6590909090909091, 0.8115942028985507, 0.72], [0.8734177215189873, 0.7045454545454545, 0.7391304347826086, 0.9199999999999999], [0.7594936708860759, 0.6818181818181818, 0.6956521739130435, 0.72], [0.7848101265822784, 0.7727272727272726, 0.782608695652174, 0.9199999999999999], [0.9620253164556961, 0.6818181818181818, 0.9565217391304347, 0.8400000000000001], [0.9746835443037974, 0.6818181818181818, 0.8840579710144927, 0.9199999999999999], [0.860759493670886, 0.6818181818181818, 0.7971014492753623, 0.8400000000000001], [0.7468354430379747, 0.6818181818181818, 0.7391304347826086, 0.72], [0.9113924050632911, 0.8181818181818181, 0.8840579710144927, 1.0], [0.7974683544303797, 0.7727272727272726, 0.8115942028985507, 0.96], [0.8481012658227848, 0.6818181818181818, 0.7536231884057971, 0.9199999999999999], [0.620253164556962, 0.5681818181818181, 0.6521739130434783, 0.6799999999999999], [0.7341772151898733, 0.6136363636363636, 0.7391304347826086, 0.76], [0.9746835443037974, 0.6363636363636362, 0.9710144927536232, 0.8], [0.8481012658227848, 0.7499999999999999, 0.8260869565217391, 1.0], [0.8227848101265822, 0.6818181818181818, 0.7971014492753623, 0.72], [0.7721518987341771, 0.5909090909090908, 0.8115942028985507, 0.5599999999999999], [0.9746835443037974, 0.8636363636363635, 0.9710144927536232, 0.8800000000000001], [0.8481012658227848, 0.7045454545454545, 0.8115942028985507, 0.96], [0.8734177215189873, 0.7272727272727273, 0.8260869565217391, 0.9199999999999999], [0.8227848101265822, 0.6818181818181818, 0.8405797101449275, 0.8800000000000001], [0.7341772151898733, 0.6136363636363636, 0.7391304347826086, 0.76], [0.8481012658227848, 0.5681818181818181, 0.8405797101449275, 0.72], [0.8481012658227848, 0.7499999999999999, 0.8260869565217391, 0.8400000000000001], [0.7721518987341771, 0.6818181818181818, 0.7101449275362319, 0.72], [0.810126582278481, 0.6136363636363636, 0.7681159420289855, 0.76], [0.8227848101265822, 0.6818181818181818, 0.7536231884057971, 0.8], [0.7215189873417721, 0.5681818181818181, 0.7246376811594203, 0.8], [0.7974683544303797, 0.7499999999999999, 0.8695652173913043, 1.0], [0.7088607594936708, 0.6363636363636362, 0.7101449275362319, 0.8], [0.8227848101265822, 0.7272727272727273, 0.7391304347826086, 0.8], [0.9113924050632911, 0.7272727272727273, 0.8695652173913043, 0.72], [0.9240506329113923, 0.6590909090909091, 0.9130434782608695, 0.72], [0.810126582278481, 0.7272727272727273, 0.7681159420289855, 0.9199999999999999]]


print the combine matrix
[[0.6455696202531644, 0.7727272727272726, 0.21739130434782608, 0.08], [0.6962025316455696, 0.5454545454545454, 0.5507246376811593, 0.44000000000000006], [0.810126582278481, 0.6363636363636362, 0.8115942028985507, 0.8400000000000001], [0.6835443037974683, 0.7727272727272726, 0.24637681159420288, 0.08], [0.7468354430379747, 0.7272727272727273, 0.6956521739130435, 0.72], [0.860759493670886, 0.7272727272727273, 0.855072463768116, 0.9199999999999999], [0.6075949367088607, 0.6818181818181818, 0.20289855072463767, 0.12], [0.6835443037974683, 0.6818181818181818, 0.6521739130434783, 0.6], [0.7341772151898733, 0.6363636363636362, 0.7391304347826086, 0.96], [0.6329113924050632, 0.6818181818181818, 0.2318840579710145, 0.08], [0.7341772151898733, 0.5909090909090908, 0.5797101449275363, 0.48], [0.9746835443037974, 0.5909090909090908, 1.0, 0.9199999999999999], [0.6329113924050632, 0.7954545454545454, 0.2318840579710145, 0.24], [0.7088607594936708, 0.6818181818181818, 0.5942028985507245, 0.52], [0.8987341772151898, 0.6818181818181818, 0.855072463768116, 0.8400000000000001], [0.5696202531645569, 0.5227272727272726, 0.18840579710144928, 0.12], [0.7215189873417721, 0.6590909090909091, 0.6086956521739131, 0.52], [0.9367088607594937, 0.6363636363636362, 0.8840579710144927, 0.76], [0.6329113924050632, 0.8181818181818181, 0.20289855072463767, 0.08], [0.7088607594936708, 0.6136363636363636, 0.6086956521739131, 0.52], [0.7974683544303797, 0.6136363636363636, 0.7101449275362319, 0.72], [0.670886075949367, 0.8409090909090908, 0.21739130434782608, 0.08], [0.6962025316455696, 0.5909090909090908, 0.6376811594202899, 0.48], [0.7848101265822784, 0.6363636363636362, 0.6956521739130435, 0.72], [0.6075949367088607, 0.7045454545454545, 0.2318840579710145, 0.08], [0.8227848101265822, 0.6363636363636362, 0.6666666666666666, 0.6], [0.810126582278481, 0.7045454545454545, 0.7971014492753623, 0.72], [0.5822784810126581, 0.7045454545454545, 0.21739130434782608, 0.08], [0.7088607594936708, 0.6818181818181818, 0.6521739130434783, 0.6], [0.9113924050632911, 0.6818181818181818, 0.8405797101449275, 0.64], [0.620253164556962, 0.8181818181818181, 0.20289855072463767, 0.04], [0.7088607594936708, 0.5681818181818181, 0.5652173913043478, 0.44000000000000006], [0.7974683544303797, 0.6363636363636362, 0.7391304347826086, 0.6], [0.5949367088607594, 0.7272727272727273, 0.2318840579710145, 0.08], [0.7848101265822784, 0.6590909090909091, 0.6231884057971014, 0.52], [1.0, 0.8636363636363635, 0.927536231884058, 0.8], [0.7215189873417721, 0.8636363636363635, 0.24637681159420288, 0.12], [0.6582278481012658, 0.6136363636363636, 0.5652173913043478, 0.5599999999999999], [0.810126582278481, 0.6363636363636362, 0.8115942028985507, 0.8800000000000001], [0.6962025316455696, 0.9545454545454545, 0.20289855072463767, 0.08], [0.6455696202531644, 0.5681818181818181, 0.43478260869565216, 0.44000000000000006], [0.7594936708860759, 0.5, 0.7246376811594203, 0.6], [0.620253164556962, 0.7045454545454545, 0.21739130434782608, 0.04], [0.7974683544303797, 0.5227272727272726, 0.6376811594202899, 0.52], [0.7974683544303797, 0.5681818181818181, 0.7246376811594203, 0.76], [0.620253164556962, 0.7045454545454545, 0.21739130434782608, 0.08], [0.7721518987341771, 0.6363636363636362, 0.6811594202898551, 0.48], [0.8734177215189873, 0.7045454545454545, 0.782608695652174, 0.8400000000000001], [0.6835443037974683, 0.8863636363636362, 0.18840579710144928, 0.16], [0.8481012658227848, 0.6818181818181818, 0.7246376811594203, 0.6799999999999999], [0.7974683544303797, 0.6590909090909091, 0.8115942028985507, 0.72], [0.6329113924050632, 0.7954545454545454, 0.18840579710144928, 0.12], [0.860759493670886, 0.6363636363636362, 0.6956521739130435, 0.5599999999999999], [0.8734177215189873, 0.7045454545454545, 0.7391304347826086, 0.9199999999999999], [0.6455696202531644, 0.8636363636363635, 0.21739130434782608, 0.12], [0.8860759493670886, 0.7272727272727273, 0.6811594202898551, 0.5599999999999999], [0.7594936708860759, 0.6818181818181818, 0.6956521739130435, 0.72], [0.6075949367088607, 0.7727272727272726, 0.27536231884057966, 0.08], [0.6329113924050632, 0.45454545454545453, 0.5072463768115941, 0.4], [0.7848101265822784, 0.7727272727272726, 0.782608695652174, 0.9199999999999999], [0.5822784810126581, 0.7727272727272726, 0.20289855072463767, 0.12], [0.7215189873417721, 0.5909090909090908, 0.5072463768115941, 0.4], [0.9620253164556961, 0.6818181818181818, 0.9565217391304347, 0.8400000000000001], [0.5569620253164557, 0.6818181818181818, 0.18840579710144928, 0.08], [0.7974683544303797, 0.5681818181818181, 0.7101449275362319, 0.6], [0.9746835443037974, 0.6818181818181818, 0.8840579710144927, 0.9199999999999999], [0.6329113924050632, 0.7499999999999999, 0.20289855072463767, 0.08], [0.7594936708860759, 0.7727272727272726, 0.6521739130434783, 0.64], [0.860759493670886, 0.6818181818181818, 0.7971014492753623, 0.8400000000000001], [0.5949367088607594, 0.7272727272727273, 0.18840579710144928, 0.08], [0.7088607594936708, 0.6590909090909091, 0.5217391304347826, 0.52], [0.7468354430379747, 0.6818181818181818, 0.7391304347826086, 0.72], [0.6835443037974683, 0.8409090909090908, 0.21739130434782608, 0.08], [0.7721518987341771, 0.6363636363636362, 0.5797101449275363, 0.52], [0.9113924050632911, 0.8181818181818181, 0.8840579710144927, 1.0], [0.5569620253164557, 0.7272727272727273, 0.18840579710144928, 0.08], [0.8354430379746834, 0.6818181818181818, 0.6376811594202899, 0.5599999999999999], [0.7974683544303797, 0.7727272727272726, 0.8115942028985507, 0.96], [0.6582278481012658, 0.9318181818181817, 0.21739130434782608, 0.04], [0.6329113924050632, 0.5227272727272726, 0.47826086956521735, 0.4], [0.8481012658227848, 0.6818181818181818, 0.7536231884057971, 0.9199999999999999], [0.6582278481012658, 0.7727272727272726, 0.20289855072463767, 0.08], [0.7848101265822784, 0.5, 0.6521739130434783, 0.6], [0.620253164556962, 0.5681818181818181, 0.6521739130434783, 0.6799999999999999], [0.6329113924050632, 0.7727272727272726, 0.21739130434782608, 0.08], [0.7721518987341771, 0.6590909090909091, 0.6811594202898551, 0.5599999999999999], [0.7341772151898733, 0.6136363636363636, 0.7391304347826086, 0.76], [0.6455696202531644, 0.7954545454545454, 0.20289855072463767, 0.08], [0.7594936708860759, 0.5, 0.5797101449275363, 0.4], [0.9746835443037974, 0.6363636363636362, 0.9710144927536232, 0.8], [0.6075949367088607, 0.7727272727272726, 0.2318840579710145, 0.08], [0.7341772151898733, 0.6136363636363636, 0.5942028985507245, 0.4], [0.8481012658227848, 0.7499999999999999, 0.8260869565217391, 1.0], [0.6329113924050632, 0.7272727272727273, 0.17391304347826086, 0.08], [0.8481012658227848, 0.7045454545454545, 0.6811594202898551, 0.6], [0.8227848101265822, 0.6818181818181818, 0.7971014492753623, 0.72], [0.6075949367088607, 0.6818181818181818, 0.20289855072463767, 0.04], [0.7215189873417721, 0.6818181818181818, 0.6086956521739131, 0.48], [0.7721518987341771, 0.5909090909090908, 0.8115942028985507, 0.5599999999999999], [0.620253164556962, 0.6818181818181818, 0.20289855072463767, 0.08], [0.7721518987341771, 0.6818181818181818, 0.6666666666666666, 0.5599999999999999], [0.9746835443037974, 0.8636363636363635, 0.9710144927536232, 0.8800000000000001], [0.6835443037974683, 0.7727272727272726, 0.21739130434782608, 0.16], [0.7215189873417721, 0.6363636363636362, 0.5942028985507245, 0.52], [0.8481012658227848, 0.7045454545454545, 0.8115942028985507, 0.96], [0.6835443037974683, 0.8863636363636362, 0.24637681159420288, 0.16], [0.810126582278481, 0.7272727272727273, 0.6521739130434783, 0.6], [0.8734177215189873, 0.7272727272727273, 0.8260869565217391, 0.9199999999999999], [0.5822784810126581, 0.7272727272727273, 0.20289855072463767, 0.08], [0.8481012658227848, 0.7045454545454545, 0.6376811594202899, 0.5599999999999999], [0.8227848101265822, 0.6818181818181818, 0.8405797101449275, 0.8800000000000001], [0.6962025316455696, 0.7954545454545454, 0.18840579710144928, 0.08], [0.7594936708860759, 0.6590909090909091, 0.6521739130434783, 0.6], [0.7341772151898733, 0.6136363636363636, 0.7391304347826086, 0.76], [0.7341772151898733, 0.9090909090909091, 0.17391304347826086, 0.08], [0.7974683544303797, 0.7499999999999999, 0.6811594202898551, 0.64], [0.8481012658227848, 0.5681818181818181, 0.8405797101449275, 0.72], [0.6582278481012658, 0.7954545454545454, 0.21739130434782608, 0.08], [0.8734177215189873, 0.7045454545454545, 0.7101449275362319, 0.6], [0.8481012658227848, 0.7499999999999999, 0.8260869565217391, 0.8400000000000001], [0.7215189873417721, 1.0, 0.21739130434782608, 0.16], [0.810126582278481, 0.6590909090909091, 0.6231884057971014, 0.52], [0.7721518987341771, 0.6818181818181818, 0.7101449275362319, 0.72], [0.6455696202531644, 0.7954545454545454, 0.20289855072463767, 0.12], [0.620253164556962, 0.5454545454545454, 0.47826086956521735, 0.4], [0.810126582278481, 0.6136363636363636, 0.7681159420289855, 0.76], [0.5443037974683543, 0.6818181818181818, 0.15942028985507248, 0.04], [0.6962025316455696, 0.5227272727272726, 0.5797101449275363, 0.52], [0.8227848101265822, 0.6818181818181818, 0.7536231884057971, 0.8], [0.5822784810126581, 0.8181818181818181, 0.14492753623188406, 0.08], [0.6962025316455696, 0.5681818181818181, 0.5797101449275363, 0.52], [0.7215189873417721, 0.5681818181818181, 0.7246376811594203, 0.8], [0.6455696202531644, 0.7499999999999999, 0.24637681159420288, 0.2], [0.6962025316455696, 0.5454545454545454, 0.5362318840579711, 0.4], [0.7974683544303797, 0.7499999999999999, 0.8695652173913043, 1.0], [0.6329113924050632, 0.7727272727272726, 0.2318840579710145, 0.16], [0.8354430379746834, 0.6590909090909091, 0.6666666666666666, 0.52], [0.7088607594936708, 0.6363636363636362, 0.7101449275362319, 0.8], [0.6455696202531644, 0.8409090909090908, 0.21739130434782608, 0.16], [0.7341772151898733, 0.6136363636363636, 0.5652173913043478, 0.48], [0.8227848101265822, 0.7272727272727273, 0.7391304347826086, 0.8], [0.6455696202531644, 0.8636363636363635, 0.27536231884057966, 0.16], [0.7215189873417721, 0.6363636363636362, 0.6521739130434783, 0.52], [0.9113924050632911, 0.7272727272727273, 0.8695652173913043, 0.72], [0.6455696202531644, 0.8636363636363635, 0.2318840579710145, 0.08], [0.7594936708860759, 0.6136363636363636, 0.7391304347826086, 0.64], [0.9240506329113923, 0.6590909090909091, 0.9130434782608695, 0.72], [0.5569620253164557, 0.6590909090909091, 0.20289855072463767, 0.08], [0.7468354430379747, 0.6818181818181818, 0.6086956521739131, 0.6], [0.810126582278481, 0.7272727272727273, 0.7681159420289855, 0.9199999999999999]]
print the values of the target array
[0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2]
print target1 values
[0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1]
print target2 values
[2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0]
print target3 values
[1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2]


print out the mix group 1
[[0.6455696202531644, 0.7727272727272726, 0.21739130434782608, 0.08], [0.6962025316455696, 0.5454545454545454, 0.5507246376811593, 0.44000000000000006], [0.810126582278481, 0.6363636363636362, 0.8115942028985507, 0.8400000000000001], [0.6835443037974683, 0.7727272727272726, 0.24637681159420288, 0.08], [0.7468354430379747, 0.7272727272727273, 0.6956521739130435, 0.72], [0.860759493670886, 0.7272727272727273, 0.855072463768116, 0.9199999999999999], [0.6075949367088607, 0.6818181818181818, 0.20289855072463767, 0.12], [0.6835443037974683, 0.6818181818181818, 0.6521739130434783, 0.6], [0.7341772151898733, 0.6363636363636362, 0.7391304347826086, 0.96], [0.6329113924050632, 0.6818181818181818, 0.2318840579710145, 0.08], [0.7341772151898733, 0.5909090909090908, 0.5797101449275363, 0.48], [0.9746835443037974, 0.5909090909090908, 1.0, 0.9199999999999999], [0.6329113924050632, 0.7954545454545454, 0.2318840579710145, 0.24], [0.7088607594936708, 0.6818181818181818, 0.5942028985507245, 0.52], [0.8987341772151898, 0.6818181818181818, 0.855072463768116, 0.8400000000000001], [0.5696202531645569, 0.5227272727272726, 0.18840579710144928, 0.12], [0.7215189873417721, 0.6590909090909091, 0.6086956521739131, 0.52], [0.9367088607594937, 0.6363636363636362, 0.8840579710144927, 0.76], [0.6329113924050632, 0.8181818181818181, 0.20289855072463767, 0.08], [0.7088607594936708, 0.6136363636363636, 0.6086956521739131, 0.52], [0.7974683544303797, 0.6136363636363636, 0.7101449275362319, 0.72], [0.670886075949367, 0.8409090909090908, 0.21739130434782608, 0.08], [0.6962025316455696, 0.5909090909090908, 0.6376811594202899, 0.48], [0.7848101265822784, 0.6363636363636362, 0.6956521739130435, 0.72], [0.6075949367088607, 0.7045454545454545, 0.2318840579710145, 0.08], [0.8227848101265822, 0.6363636363636362, 0.6666666666666666, 0.6], [0.810126582278481, 0.7045454545454545, 0.7971014492753623, 0.72], [0.5822784810126581, 0.7045454545454545, 0.21739130434782608, 0.08], [0.7088607594936708, 0.6818181818181818, 0.6521739130434783, 0.6], [0.9113924050632911, 0.6818181818181818, 0.8405797101449275, 0.64], [0.620253164556962, 0.8181818181818181, 0.20289855072463767, 0.04], [0.7088607594936708, 0.5681818181818181, 0.5652173913043478, 0.44000000000000006], [0.7974683544303797, 0.6363636363636362, 0.7391304347826086, 0.6], [0.5949367088607594, 0.7272727272727273, 0.2318840579710145, 0.08], [0.7848101265822784, 0.6590909090909091, 0.6231884057971014, 0.52], [1.0, 0.8636363636363635, 0.927536231884058, 0.8], [0.7215189873417721, 0.8636363636363635, 0.24637681159420288, 0.12], [0.6582278481012658, 0.6136363636363636, 0.5652173913043478, 0.5599999999999999], [0.810126582278481, 0.6363636363636362, 0.8115942028985507, 0.8800000000000001], [0.6962025316455696, 0.9545454545454545, 0.20289855072463767, 0.08], [0.6455696202531644, 0.5681818181818181, 0.43478260869565216, 0.44000000000000006], [0.7594936708860759, 0.5, 0.7246376811594203, 0.6], [0.620253164556962, 0.7045454545454545, 0.21739130434782608, 0.04], [0.7974683544303797, 0.5227272727272726, 0.6376811594202899, 0.52], [0.7974683544303797, 0.5681818181818181, 0.7246376811594203, 0.76], [0.620253164556962, 0.7045454545454545, 0.21739130434782608, 0.08], [0.7721518987341771, 0.6363636363636362, 0.6811594202898551, 0.48], [0.8734177215189873, 0.7045454545454545, 0.782608695652174, 0.8400000000000001], [0.6835443037974683, 0.8863636363636362, 0.18840579710144928, 0.16], [0.8481012658227848, 0.6818181818181818, 0.7246376811594203, 0.6799999999999999]]
print sections testing, which will be later use to compare the predicted values
print group 3, section 1










    
    

















    
    






print group 1, section 2










    
    

















    
    






print group 2, section 3










    
    

















    
    






The error for the first section of testing
The first section of testing use the first and second group for training, and the third group for testing
-0.18403548603555775
0.33765491027094363
-0.7042322672613793
-0.18950573503633072
0.327564731374008
-0.6995209989070915
-0.18178359110504583
0.32867074965843834
-0.7102391333153932
-0.1824874203689576
0.3263334127978278
-0.7053376458808089
-0.18496275925170902
0.3178874552807459
-0.70114384829219
-0.18004090939745665
0.3219365026665114
-0.7053208938613037
-0.17914164962012036
0.3278065765115784
-0.6959616530604241
-0.185684333083743
0.3193272993440359
-0.7056213199204058
-0.2006135902736097
0.32058345559672485
-0.7151000105009904
-0.19413193507730786
0.3228034604913693
-0.709326565342849
-0.19272050279831432
0.31631100419889857
-0.7043929549865887
-0.19654632753121826
0.3297660704787031
-0.7050893849441465
-0.18367489168038575
0.3175164101158666
-0.7030354778146358
-0.1913725186716827
0.32360825168496166
-0.7002296067922305
-0.18759141290519066
0.3284728940148799
-0.7029090521919844
-0.18299958062437616
0.3282241176800864
-0.7132808533320417
-0.18601048161704825
0.3255969809623023










    
    

















    
    






The error for the second section of testing
In this section, will use the second and third for training and use the first group for testing
-0.23460335945768263
-0.7537931480871178
0.3229465859187354
-0.21516639746448163
0.3290304591349741
-0.7518103134678946
0.29012688258988806
-0.7537794628629144
0.31548426154771503
-0.7423788477342264
-0.7507206716517568
-0.21946850833026732
-0.7667324558922373
0.3006690648153836
-0.20639891703530022
0.31145636973874724
-0.7468394253112335
-0.19636022273146492
-0.22275068211264648
-0.751476357150755
-0.19339480584653035
0.3114471351205079
-0.21627293897856592
-0.22570342167039814
-0.7462868143378751
-0.20265815865098347
-0.7408080069480993
-0.7402842970118212
-0.7338596829924928
0.3195175779099012
-0.2142940015194661
-0.7528864649207461
-0.21188391729902012
0.3162457334112022
-0.207616867334384
-0.21210346598143248
0.30643682528644345
0.337038099626639
-0.2079007031819139
0.29983317171638224
0.31239034313180425
0.30052257779430336
-0.7372239936719218
-0.7336023726203186
-0.20713800281806405
-0.7451199230014328
0.30031003946609025
0.3203979855516095
-0.7515769020519997
-0.21816316377985923










    
    

















    
    






The third section uses the first and the third group for training and the second group for testing
print the error for the third section
-0.7587737557758
-0.207543620325496
0.3049583391689673
-0.22303614921540882
0.3143436955972315
0.3042249388379642
-0.20527180282015678
0.36192159377335575
-0.20638961311217074
-0.7739028400155615
-0.21721853752410625
-0.21358072163899688
-0.7652261341982136
-0.7723671742366043
0.30528294782173127
-0.19956838124639265
-0.7720340677769093
-0.7644307017827842
-0.2474507135247238
-0.2202030421468969
0.28945021201227483
-0.7751801494654229
-0.7463216146850821
0.3493795720775794
-0.2139119110813431
-0.7718915865655638
-0.7934210390419774
-0.22500044430540356
0.339899805243813
-0.2343014416476361
-0.218535361476217
0.32352598144733846
-0.7459387410115759
-0.18994010853643994
0.3345750440602726
-0.7751986051004337
-0.18537856175003098
-0.7564876995446841
-0.2071240870794192
0.3055094696665491
-0.7512560372732892
0.3283256292885489
-0.2299022975700964
-0.7708103752483598
-0.7557351299538319
0.3359405738774216
0.3220701527416756
0.322090580522051
-0.7627443436177018
0.3295009883882287










    
    



























In [8]:



     


import pandas as pd
from PIL import Image
from numpy import asarray
import numpy as np
import math
import matplotlib.pyplot as plt
import os
import sklearn

graph1 = [0, 0, 0, 0, 0]


#initialize the actual Y value for the output
ob = [1, 2, 5, 8, 11]

# get the maximum value of the array
max_value = np.max(ob)

#normalize the data
count = 0
while count < len(ob):
    ob[count] = ob[count]/max_value
    count += 1
    
    


print("print out normalize observe")
print(ob)
#initialize the actual X value for the input
inp = [1, 2, 3, 4, 5]

#normalize the data
count = 0
while count < len(ob):
    inp[count] = inp[count]/max_value
    count += 1
    
    
#initializing random weights
w1 = 0.3
w2 = 0.4
w3 = 0.5
w4 = 0.2
w5 = 0.7
w6 = 0.6
w7 = 0.3
w8 = 0.1

#initialize the bias to be a constant 1
bias = 1

#intitialize the weights of the bias
bw1 = 0.2
bw2 = 0.5
bw3 = 0.7
bw4 = 0.3
bw5 = 0.4



#this will save the X values at each node
array1 = 1
array2 = 1
array3 = 1
array4 = 1
array5 = 1



#this will save the Y values at each node
Node1 = 1
Node2 = 1
Node3 = 1
Node4 = 1
Node5 = 1



#intialize the learning rate
learningrate = 0.03


#initialize the actual Y value for the output
observe = 1

# I know after you have to regularize the data of observe


#initialize the actual X value for the input/ you don't regularize the X input
arr = 1

#Graph the X and Y values 
print('\n')
plt.xlabel('Xvalues')
plt.ylabel('Yvalues')
plt.scatter(inp, ob)
plt.show()


index = 0
# Train your data
epoch = 0
while epoch < 200:
    inp, ob = sklearn.utils.shuffle(inp, ob)
    index = 0
    print(inp)
    print(ob)
    while index < len(inp):
        arr = inp[index]
        observe = ob[index]
    

        # Weight1 multiplied by the values of the input array
        array1 = w1 * arr + bias * bw1
    
        # Weight2 multiplied by the vales of the input array
        array2 = w2 * arr + bias * bw2
    
        #print(array1)
    


   
        # Node 1: takes in the input X and passes it through the sigmoid function then stores it at the corresponding Y
        Node1 = 1 / (1 + math.exp(-(array1)))
 
        # Node 2: takes in the input X and passes it through the sigmoid function then stores it at the correspodning X
        Node2 = 1 / (1 + math.exp(-array2))


    
    
    
        array3 = w3 * Node1 + w4 * Node2 + bias * bw3

        array4 = w5 * Node1 + w6 * Node2 + bias * bw4

        #count = 0
    
        # function 
        # Node 3 and 4
        Node3 = 1 / (1 + math.exp(-array3))
    
        # Node 2
        Node4 = 1 / (1 + math.exp(-array4))
    





        array5 = w7 * Node3 + w8 * Node4 + bias * bw5
 
        # Node 5
        Node5 = 1 / (1 + math.exp(-array5))
        # The predicted Y values are stores at node 5
        predicted = Node5
    
    
        error = observe - predicted 
    
        print(error)
   
    

        # intiliazing a storage for each weight, which you will use to obtain the new weight in gradient descent
        w1old = w1
        w2old = w2
        w3old = w3
        w4old = w4
        w5old = w5
        w6old = w6
        w7old = w7
        w8old = w8    
    
    
        oldbw1 = bw1
        oldbw2 = bw2
        oldbw3 = bw3
        oldbw4 = bw4
        oldbw5 = bw5
    
    
    
    
        #gradient descent
    
        # Get the derivative for each node using it's correspoding X values
        s   = 1/(1 + math.exp(-array5))
        dernode5 = s*(1-s)
        L5  = dernode5 * error
        bw5 = learningrate * L5 * bias
        w7  = learningrate * L5 * Node3
        w8  = learningrate * L5 * Node4
    
    
        s   = 1/(1 + math.exp(-array3))
        dernode3 = s*(1-s)
        L3  = dernode3 * ( L5 * w7)
        bw3 = learningrate * L3 * bias
        w3  = learningrate * L3 * Node1
        w4  = learningrate * L3 * Node2
    
    
        s   = 1/(1 + math.exp(-array4))
        dernode4 = s*(1-s)
        L4  = dernode4 * (L5 * w8)
        bw4 = learningrate * L4 * bias
        w5  = learningrate * L4 * Node1
        w6  = learningrate * L4 * Node2
    
    
        s   = 1/(1 + math.exp(-array1))
        dernode1 = s*(1-s)
        L1  = dernode1 * ((L3 * w3) + (L4 * w5))
        bw1 = learningrate * L1 * bias
        w1  = learningrate * L1 * arr
    
    
        s   = 1/(1 + math.exp(-array2))
        dernode2 = s*(1-s)
        L2  = dernode2 * ((L3 * w4) + (L4 * w6))
        bw2 = learningrate * L2 * bias
        w2  = learningrate * L2 * arr
     
    
        #Using gradient descent to find the where the slope equals to zero fast for each weight.
        w1 = w1 + w1old
        w2 = w2 + w2old
        w3 = w3 + w3old
        w4 = w4 + w4old
        w5 = w5 + w5old
        w6 = w6 + w6old
        w7 = w7 + w7old
        w8 = w8 + w8old
    
    
        bw1 = bw1 + oldbw1
        bw2 = bw2 + oldbw2
        bw3 = bw3 + oldbw3
        bw4 = bw4 + oldbw4
        bw5 = bw5 + oldbw5
        
        index += 1
 
    
   
    epoch += 1

    
print("print out the length")
print(len(inp))    

    
index = 0
while index < len(inp):
    
    arr = inp[index]
    
    # Weight1 multiplied by the values of the input array
    array1 = w1 * arr + bias * bw1
    
    # Weight2 multiplied by the vales of the input array
    array2 = w2 * arr + bias * bw2
    
    #print(array1)
    


   
    # Node 1: takes in the input X and passes it through the sigmoid function then stores it at the corresponding Y
    Node1 = 1 / (1 + math.exp(-(array1)))
 
    # Node 2: takes in the input X and passes it through the sigmoid function then stores it at the correspodning X
    Node2 = 1 / (1 + math.exp(-array2))


    
    
    
    array3 = w3 * Node1 + w4 * Node2 + bias * bw3

    array4 = w5 * Node1 + w6 * Node2 + bias * bw4

    count = 0
    
    # function 
    # Node 3 and 4
    Node3 = 1 / (1 + math.exp(-array3))
    
    # Node 2
    Node4 = 1 / (1 + math.exp(-array4))
    





    array5 = w7 * Node3 + w8 * Node4 + bias * bw5
 
    # Node 5
    Node5 = 1 / (1 + math.exp(-array5))
    # The predicted Y values are stores at node 5
    
    graph1[index] = Node5
    index += 1

    
#count = 0
#while count < len(graph1):
#    graph1[count] = graph1[count] * max_value
#    count += 1

plt.xlabel('TrainXvalues')
plt.ylabel('TrainYvalues')
plt.scatter(inp, graph1)



     


















    
    






print out normalize observe
[0.09090909090909091, 0.18181818181818182, 0.45454545454545453, 0.7272727272727273, 1.0]












    
    

















    
    






[0.09090909090909091, 0.45454545454545453, 0.18181818181818182, 0.36363636363636365, 0.2727272727272727]
[0.09090909090909091, 1.0, 0.18181818181818182, 0.7272727272727273, 0.45454545454545453]
-0.5772242962461137
0.3332693549964947
-0.4856621094983305
0.06111663705687931
-0.2117037974020644
[0.18181818181818182, 0.36363636363636365, 0.45454545454545453, 0.09090909090909091, 0.2727272727272727]
[0.18181818181818182, 0.7272727272727273, 1.0, 0.09090909090909091, 0.45454545454545453]
-0.4836625231114681
0.06312234086069946
0.33555157308578476
-0.5742076898789028
-0.2089573131447537
[0.2727272727272727, 0.36363636363636365, 0.09090909090909091, 0.45454545454545453, 0.18181818181818182]
[0.45454545454545453, 0.7272727272727273, 0.09090909090909091, 1.0, 0.18181818181818182]
-0.20829253623327654
0.06500200927649347
-0.5712759762903866
0.3392504553321314
-0.47973341760924587
[0.09090909090909091, 0.18181818181818182, 0.2727272727272727, 0.45454545454545453, 0.36363636363636365]
[0.09090909090909091, 0.18181818181818182, 0.45454545454545453, 1.0, 0.7272727272727273]
-0.5690127914067122
-0.47637565807718857
-0.2022047940626977
0.3437232312263193
0.06996837920054699
[0.18181818181818182, 0.2727272727272727, 0.45454545454545453, 0.36363636363636365, 0.09090909090909091]
[0.18181818181818182, 0.45454545454545453, 1.0, 0.7272727272727273, 0.09090909090909091]
-0.4755265763180027
-0.20135408725410503
0.3445741270139001
0.07081316853990671
-0.5655016692900137
[0.09090909090909091, 0.36363636363636365, 0.18181818181818182, 0.2727272727272727, 0.45454545454545453]
[0.09090909090909091, 0.7272727272727273, 0.18181818181818182, 0.45454545454545453, 1.0]
-0.5636716165317085
0.07425572151870075
-0.47126316677898167
-0.197082919503979
0.3488460095048289
[0.18181818181818182, 0.2727272727272727, 0.36363636363636365, 0.45454545454545453, 0.09090909090909091]
[0.18181818181818182, 0.45454545454545453, 0.7272727272727273, 1.0, 0.09090909090909091]
-0.47022267790754363
-0.19604064041854902
0.07724669429205
0.34963270952314274
-0.5602626603023871
[0.2727272727272727, 0.45454545454545453, 0.36363636363636365, 0.09090909090909091, 0.18181818181818182]
[0.45454545454545453, 1.0, 0.7272727272727273, 0.09090909090909091, 0.18181818181818182]
-0.194962026868927
0.3509670951832372
0.07716015896003059
-0.5591957446617574
-0.4665328117514235
[0.09090909090909091, 0.36363636363636365, 0.18181818181818182, 0.45454545454545453, 0.2727272727272727]
[0.09090909090909091, 0.7272727272727273, 0.18181818181818182, 1.0, 0.45454545454545453]
-0.5558108876481109
0.0821491651803784
-0.46341465734501647
0.3560710862668166
-0.19041769411177728
[0.36363636363636365, 0.45454545454545453, 0.2727272727272727, 0.18181818181818182, 0.09090909090909091]
[0.7272727272727273, 1.0, 0.45454545454545453, 0.18181818181818182, 0.09090909090909091]
0.08286508240053281
0.3552332202139047
-0.1912487944611903
-0.46325423302895047
-0.5525330980806256
[0.36363636363636365, 0.2727272727272727, 0.18181818181818182, 0.45454545454545453, 0.09090909090909091]
[0.7272727272727273, 0.45454545454545453, 0.18181818181818182, 1.0, 0.09090909090909091]
0.08543990632774989
-0.18749619254695887
-0.45951150982453653
0.3599863110437985
-0.550010355828767
[0.2727272727272727, 0.18181818181818182, 0.36363636363636365, 0.09090909090909091, 0.45454545454545453]
[0.45454545454545453, 0.18181818181818182, 0.7272727272727273, 0.09090909090909091, 1.0]
-0.1846776515085578
-0.4567006400761728
0.09015290535274234
-0.5462895479076348
0.364361033954711
[0.09090909090909091, 0.2727272727272727, 0.45454545454545453, 0.18181818181818182, 0.36363636363636365]
[0.09090909090909091, 0.45454545454545453, 1.0, 0.18181818181818182, 0.7272727272727273]
-0.5456786635772555
-0.18033365403792906
0.36559412294150373
-0.4536240907223604
0.09323565614556562
[0.09090909090909091, 0.18181818181818182, 0.2727272727272727, 0.36363636363636365, 0.45454545454545453]
[0.09090909090909091, 0.18181818181818182, 0.45454545454545453, 0.7272727272727273, 1.0]
-0.5432277527819124
-0.4505313613373712
-0.17632301388138333
0.09694503627072082
0.3692669143165134
[0.45454545454545453, 0.09090909090909091, 0.36363636363636365, 0.18181818181818182, 0.2727272727272727]
[1.0, 0.09090909090909091, 0.7272727272727273, 0.18181818181818182, 0.45454545454545453]
0.36798503692472184
-0.5420903468526451
0.09592121828937972
-0.4497234469758606
-0.17551433172606817
[0.45454545454545453, 0.2727272727272727, 0.36363636363636365, 0.09090909090909091, 0.18181818181818182]
[1.0, 0.45454545454545453, 0.7272727272727273, 0.09090909090909091, 0.18181818181818182]
0.37041197047712293
-0.17619271208694182
0.09707516850092845
-0.5394145537129162
-0.44671173512542056
[0.09090909090909091, 0.18181818181818182, 0.2727272727272727, 0.36363636363636365, 0.45454545454545453]
[0.09090909090909091, 0.18181818181818182, 0.45454545454545453, 0.7272727272727273, 1.0]
-0.5360012572246629
-0.4432931854556284
-0.16907875834362268
0.10417993699965888
0.3764773210801118
[0.45454545454545453, 0.18181818181818182, 0.2727272727272727, 0.36363636363636365, 0.09090909090909091]
[1.0, 0.18181818181818182, 0.45454545454545453, 0.7272727272727273, 0.09090909090909091]
0.3751497234108897
-0.44415423645604585
-0.16994043923317564
0.1033194213576456
-0.5332136190692446
[0.09090909090909091, 0.2727272727272727, 0.18181818181818182, 0.36363636363636365, 0.45454545454545453]
[0.09090909090909091, 0.45454545454545453, 0.18181818181818182, 0.7272727272727273, 1.0]
-0.5313471270411867
-0.16596670735312263
-0.43804595359500537
0.10884015474506414
0.3811215398468094
[0.09090909090909091, 0.36363636363636365, 0.2727272727272727, 0.18181818181818182, 0.45454545454545453]
[0.09090909090909091, 0.7272727272727273, 0.45454545454545453, 0.18181818181818182, 1.0]
-0.5290836523692432
0.10897019292058396
-0.1640858981713375
-0.4361712895221011
0.3833881233244807
[0.2727272727272727, 0.36363636363636365, 0.45454545454545453, 0.18181818181818182, 0.09090909090909091]
[0.45454545454545453, 0.7272727272727273, 1.0, 0.18181818181818182, 0.09090909090909091]
-0.16332145369787504
0.10992897095455467
0.382206586836231
-0.43716073322111854
-0.5264613689515397
[0.09090909090909091, 0.45454545454545453, 0.2727272727272727, 0.36363636363636365, 0.18181818181818182]
[0.09090909090909091, 1.0, 0.45454545454545453, 0.7272727272727273, 0.18181818181818182]
-0.5245929003109242
0.3861472133158089
-0.1605845999151551
0.1126616409610589
-0.43308485214288633
[0.09090909090909091, 0.36363636363636365, 0.45454545454545453, 0.18181818181818182, 0.2727272727272727]
[0.09090909090909091, 0.7272727272727273, 1.0, 0.18181818181818182, 0.45454545454545453]
-0.5223913202835259
0.11568176715672684
0.3879393817356007
-0.4314793533083087
-0.1572590917246392
[0.45454545454545453, 0.18181818181818182, 0.09090909090909091, 0.2727272727272727, 0.36363636363636365]
[1.0, 0.18181818181818182, 0.09090909090909091, 0.45454545454545453, 0.7272727272727273]
0.3886572779823807
-0.4307678845109271
-0.5200779447108757
-0.1546753749764816
0.11856130841966228
[0.2727272727272727, 0.18181818181818182, 0.45454545454545453, 0.36363636363636365, 0.09090909090909091]
[0.45454545454545453, 0.18181818181818182, 1.0, 0.7272727272727273, 0.09090909090909091]
-0.15454540661626187
-0.4266632858189197
0.39291601299114765
0.11880726926337781
-0.5178354348180582
[0.09090909090909091, 0.36363636363636365, 0.18181818181818182, 0.2727272727272727, 0.45454545454545453]
[0.09090909090909091, 0.7272727272727273, 0.18181818181818182, 0.45454545454545453, 1.0]
-0.5159672263646884
0.12212273636560478
-0.4236826879356343
-0.14946129532962577
0.39644883403293474
[0.36363636363636365, 0.2727272727272727, 0.18181818181818182, 0.09090909090909091, 0.45454545454545453]
[0.7272727272727273, 0.45454545454545453, 0.18181818181818182, 0.09090909090909091, 1.0]
0.12231500236817094
-0.15081173402781917
-0.42294290072684576
-0.5122666287271361
0.39851671390521326
[0.2727272727272727, 0.36363636363636365, 0.09090909090909091, 0.18181818181818182, 0.45454545454545453]
[0.45454545454545453, 0.7272727272727273, 0.09090909090909091, 0.18181818181818182, 1.0]
-0.14831435890063033
0.12491123392255987
-0.5117754408850804
-0.41904375899808305
0.4005493368430212
[0.36363636363636365, 0.45454545454545453, 0.18181818181818182, 0.2727272727272727, 0.09090909090909091]
[0.7272727272727273, 1.0, 0.18181818181818182, 0.45454545454545453, 0.09090909090909091]
0.12638651469885853
0.3986063996339396
-0.42090765214659126
-0.14668638410343365
-0.5096982861981809
[0.09090909090909091, 0.18181818181818182, 0.36363636363636365, 0.2727272727272727, 0.45454545454545453]
[0.09090909090909091, 0.18181818181818182, 0.7272727272727273, 0.45454545454545453, 1.0]
-0.5078331477807022
-0.415099855979193
0.13180981346385157
-0.14136801226057644
0.40453442330254485
[0.2727272727272727, 0.09090909090909091, 0.18181818181818182, 0.36363636363636365, 0.45454545454545453]
[0.45454545454545453, 0.09090909090909091, 0.18181818181818182, 0.7272727272727273, 1.0]
-0.14234455207377245
-0.505376803159473
-0.41264281756520854
0.13426832580782044
0.406460128757399
[0.36363636363636365, 0.09090909090909091, 0.18181818181818182, 0.2727272727272727, 0.45454545454545453]
[0.7272727272727273, 0.09090909090909091, 0.18181818181818182, 0.45454545454545453, 1.0]
0.1322557918397106
-0.5044842100328452
-0.4117500351023439
-0.1375310301069504
0.4083673470114889
[0.2727272727272727, 0.09090909090909091, 0.18181818181818182, 0.36363636363636365, 0.45454545454545453]
[0.45454545454545453, 0.09090909090909091, 0.18181818181818182, 0.7272727272727273, 1.0]
-0.1385419718299467
-0.5015923724727692
-0.40885778415533386
0.138055232820677
0.41023346898823254
[0.36363636363636365, 0.45454545454545453, 0.18181818181818182, 0.2727272727272727, 0.09090909090909091]
[0.7272727272727273, 1.0, 0.18181818181818182, 0.45454545454545453, 0.09090909090909091]
0.13600281996058228
0.4081883986009339
-0.41141067673658155
-0.13719182168785954
-0.5002487245034121
[0.09090909090909091, 0.18181818181818182, 0.2727272727272727, 0.45454545454545453, 0.36363636363636365]
[0.09090909090909091, 0.18181818181818182, 0.45454545454545453, 1.0, 0.7272727272727273]
-0.4983905303790146
-0.4056558700279144
-0.131439958413676
0.4144514612474539
0.14019159178724794
[0.45454545454545453, 0.18181818181818182, 0.09090909090909091, 0.2727272727272727, 0.36363636363636365]
[1.0, 0.18181818181818182, 0.09090909090909091, 0.45454545454545453, 0.7272727272727273]
0.412362147950169
-0.407273726364688
-0.49663158639726457
-0.13119873566384616
0.14199315683517688
[0.18181818181818182, 0.36363636363636365, 0.45454545454545453, 0.09090909090909091, 0.2727272727272727]
[0.18181818181818182, 0.7272727272727273, 1.0, 0.09090909090909091, 0.45454545454545453]
-0.40393825567556146
0.14297648137238694
0.41513703257608225
-0.4954025405597098
-0.12996868754401242
[0.45454545454545453, 0.2727272727272727, 0.18181818181818182, 0.36363636363636365, 0.09090909090909091]
[1.0, 0.45454545454545453, 0.18181818181818182, 0.7272727272727273, 0.09090909090909091]
0.4159209294052353
-0.13104772263524428
-0.4032542148694664
0.14366068602506432
-0.4931628705062925
[0.09090909090909091, 0.2727272727272727, 0.45454545454545453, 0.18181818181818182, 0.36363636363636365]
[0.09090909090909091, 0.45454545454545453, 1.0, 0.18181818181818182, 0.7272727272727273]
-0.4913122194118763
-0.1258754069419888
0.4200091009995768
-0.3996937285218661
0.14722186831349293
[0.36363636363636365, 0.09090909090909091, 0.18181818181818182, 0.2727272727272727, 0.45454545454545453]
[0.7272727272727273, 0.09090909090909091, 0.18181818181818182, 0.45454545454545453, 1.0]
0.14666358449711914
-0.4901820932399409
-0.3974490528948535
-0.12323926608617769
0.4226418054998502
[0.2727272727272727, 0.36363636363636365, 0.45454545454545453, 0.18181818181818182, 0.09090909090909091]
[0.45454545454545453, 0.7272727272727273, 1.0, 0.18181818181818182, 0.09090909090909091]
-0.12437910001292812
0.14879790633375545
0.42093750362916316
-0.39877337886837644
-0.48815341697398984
[0.09090909090909091, 0.36363636363636365, 0.2727272727272727, 0.45454545454545453, 0.18181818181818182]
[0.09090909090909091, 0.7272727272727273, 0.45454545454545453, 1.0, 0.18181818181818182]
-0.48630928307303556
0.15183856665126716
-0.12144835926418801
0.42443029888898787
-0.39531089828634974
[0.45454545454545453, 0.18181818181818182, 0.36363636363636365, 0.2727272727272727, 0.09090909090909091]
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[0.09090909090909091, 1.0, 0.18181818181818182, 0.7272727272727273, 0.45454545454545453]
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[0.45454545454545453, 0.2727272727272727, 0.36363636363636365, 0.09090909090909091, 0.18181818181818182]
[1.0, 0.45454545454545453, 0.7272727272727273, 0.09090909090909091, 0.18181818181818182]
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[0.45454545454545453, 0.7272727272727273, 0.09090909090909091, 0.18181818181818182, 1.0]
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[0.18181818181818182, 0.45454545454545453, 0.7272727272727273, 1.0, 0.09090909090909091]
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[0.18181818181818182, 0.45454545454545453, 0.7272727272727273, 0.09090909090909091, 1.0]
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print out the length
5










    
    
Out[8]:








<matplotlib.collections.PathCollection at 0x266744b2af0>










    
    



























In [24]:



     


import pandas as pd
from PIL import Image
from numpy import asarray
import numpy as np
import math
import matplotlib.pyplot as plt
import os
import sklearn


inp = [1, 2, 3, 4, 5]
ob = [1, 2, 5, 8, 11]



inp, ob = sklearn.utils.shuffle(inp, ob)
#inp, ob = np.random.shuffle(inp, ob)
print(inp)
print(ob)



     


















    
    






[3, 4, 5, 1, 2]
[5, 8, 11, 1, 2]




















In [67]:



     


import pandas as pd
from PIL import Image
from numpy import asarray
import numpy as np
import math
import matplotlib.pyplot as plt
import os
import sklearn

graph1 = [0 for i in range(100)]

######################################################
i = 0
X_values = [] * 500
while i <= 100: 
      i += 0.2
      X_values.append(i)
      


count = 0
Y_values = [] * 500
while count <= 499:
    Y_values.append(1/X_values[count])
    count += 1

print('\n')
print('\n')
plt.figure(figsize=(20,10))
plt.xlabel('X_values')
plt.ylabel('Y_values')
#plt.plot(X_values, Y_values)
plt.scatter(X_values, Y_values)
plt.show()

count = 0
Test_Xvalues = [] * 100
Test_Yvalues = [] * 100
Training_Xvalues = [] * 400
Training_Yvalues = [] * 400

while count <= 499:
      if count % 5 == 0:
            Test_Xvalues.append(X_values[count])
          
            Test_Yvalues.append(Y_values[count])
      else:
        Training_Xvalues.append(X_values[count])
       
        Training_Yvalues.append(Y_values[count])    
     
      count += 1

        
        
print("length of input testing")
x1 = len(Test_Xvalues)

print("length of output testing")
x2 = len(Test_Yvalues)

print("length of input training")
x3 = len(Training_Xvalues)

print("length of output training")
x4 = len(Training_Yvalues)

print("total length of training and testing")
x5 = len(X_values)



#normalizing the dataset
count = 0
while count < 400:
    Training_Yvalues[count] = Training_Yvalues[count] / 100
    count += 1

count = 0
while count < 100:
    Test_Yvalues[count] = Test_Yvalues[count] / 100
    count += 1

print('\n')
print(x1)
print(x2)
print(x3)
print(x4)
print(x5)

print('\n')
print('\n')
#plt.figure(figsize=(20,10))
plt.xlabel('Training_Xvalues')
plt.ylabel('Training_Yvalues')
plt.scatter(Training_Xvalues, Training_Yvalues)
plt.show()

print('\n')
#plt.figure(figsize=(20,10))
plt.xlabel('Test_Xvalues')
plt.ylabel('Test_Yvalues')
plt.scatter(Test_Xvalues, Test_Yvalues)
plt.show()

#########################################################







#initializing random weights
w1 = 0.3
w2 = 0.4
w3 = 0.5
w4 = 0.2
w5 = 0.7
w6 = 0.6
w7 = 0.3
w8 = 0.1

#initialize the bias to be a constant 1
bias = 1

#intitialize the weights of the bias
bw1 = 0.2
bw2 = 0.5
bw3 = 0.7
bw4 = 0.3
bw5 = 0.4



#this will save the X values at each node
array1 = 1
array2 = 1
array3 = 1
array4 = 1
array5 = 1



#this will save the Y values at each node
Node1 = 1
Node2 = 1
Node3 = 1
Node4 = 1
Node5 = 1



#intialize the learning rate
learningrate = 0.1


#initialize the actual Y value for the output
observe = 1

# I know after you have to regularize the data of observe


#initialize the actual X value for the input/ you don't regularize the X input
arr = 1

#Graph the X and Y values 
print('\n')
plt.xlabel('Xvalues')
plt.ylabel('Yvalues')
plt.scatter(inp, ob)
plt.show()


index = 0
# Train your data
epoch = 0
while epoch < 200:
    Training_Xvalues, Training_Yvalues = sklearn.utils.shuffle(Training_Xvalues, Training_Yvalues)
    index = 0

    while index < len(Training_Xvalues):
        arr = Training_Xvalues[index]
        observe = Training_Yvalues[index]
    

        # Weight1 multiplied by the values of the input array
        array1 = w1 * arr + bias * bw1
    
        # Weight2 multiplied by the vales of the input array
        array2 = w2 * arr + bias * bw2
    
        #print(array1)
    


   
        # Node 1: takes in the input X and passes it through the sigmoid function then stores it at the corresponding Y
        Node1 = 1 / (1 + math.exp(-(array1)))
 
        # Node 2: takes in the input X and passes it through the sigmoid function then stores it at the correspodning X
        Node2 = 1 / (1 + math.exp(-array2))


    
    
    
        array3 = w3 * Node1 + w4 * Node2 + bias * bw3

        array4 = w5 * Node1 + w6 * Node2 + bias * bw4

        count = 0
    
        # function 
        # Node 3 and 4
        Node3 = 1 / (1 + math.exp(-array3))
    
        # Node 2
        Node4 = 1 / (1 + math.exp(-array4))
    





        array5 = w7 * Node3 + w8 * Node4 + bias * bw5
 
        # Node 5
        Node5 = 1 / (1 + math.exp(-array5))
        # The predicted Y values are stores at node 5
        predicted = Node5
    
    
        error = observe - predicted 
    
        print(error)
   
    

        # intiliazing a storage for each weight, which you will use to obtain the new weight in gradient descent
        w1old = w1
        w2old = w2
        w3old = w3
        w4old = w4
        w5old = w5
        w6old = w6
        w7old = w7
        w8old = w8    
    
    
        oldbw1 = bw1
        oldbw2 = bw2
        oldbw3 = bw3
        oldbw4 = bw4
        oldbw5 = bw5
    
    
    
    
        #gradient descent
    
        # Get the derivative for each node using it's correspoding X values
        s   = 1/(1 + math.exp(-array5))
        dernode5 = s*(1-s)
        L5  = dernode5 * error
        bw5 = learningrate * L5 * error
        w7  = learningrate * L5 * Node3
        w8  = learningrate * L5 * Node4
    
    
        s   = 1/(1 + math.exp(-array3))
        dernode3 = s*(1-s)
        L3  = dernode3 * ( L5 * w7)
        bw3 = learningrate * L3 * bias
        w3  = learningrate * L3 * Node1
        w4  = learningrate * L3 * Node2
    
    
        s   = 1/(1 + math.exp(-array4))
        dernode4 = s*(1-s)
        L4  = dernode4 * (L5 * w8)
        bw4 = learningrate * L4 * bias
        w5  = learningrate * L4 * Node1
        w6  = learningrate * L4 * Node2
    
    
        s   = 1/(1 + math.exp(-array1))
        dernode1 = s*(1-s)
        L1  = dernode1 * (L3 * w3) * (L4 * w5)
        bw1 = learningrate * L1 * bias
        w1  = learningrate * L1 * arr
    
    
        s   = 1/(1 + math.exp(-array2))
        dernode2 = s*(1-s)
        L2  = dernode2 * (L3 * w4) * (L4 * w6)
        bw2 = learningrate * L2 * bias
        w2  = learningrate * L2 * arr
     
    
        #Using gradient descent to find the where the slope equals to zero fast for each weight.
        w1 = w1 + w1old
        w2 = w2 + w2old
        w3 = w3 + w3old
        w4 = w4 + w4old
        w5 = w5 + w5old
        w6 = w6 + w6old
        w7 = w7 + w7old
        w8 = w8 + w8old
    
    
        bw1 = bw1 + oldbw1
        bw2 = bw2 + oldbw2
        bw3 = bw3 + oldbw3
        bw4 = bw4 + oldbw4
        bw5 = bw5 + oldbw5
        
        index += 1
 
    
   
    epoch += 1

       
print("print Test_values length")
print(len(Test_Xvalues))

print("print graph1 length")
print(len(graph1))
    
index = 0
while index < len(Test_Xvalues):
    
    arr = Test_Xvalues[index]
    
    # Weight1 multiplied by the values of the input array
    array1 = w1 * arr + bias * bw1
    
    # Weight2 multiplied by the vales of the input array
    array2 = w2 * arr + bias * bw2
    
    #print(array1)
    


   
    # Node 1: takes in the input X and passes it through the sigmoid function then stores it at the corresponding Y
    Node1 = 1 / (1 + math.exp(-(array1)))
 
    # Node 2: takes in the input X and passes it through the sigmoid function then stores it at the correspodning X
    Node2 = 1 / (1 + math.exp(-array2))


    
    
    
    array3 = w3 * Node1 + w4 * Node2 + bias * bw3

    array4 = w5 * Node1 + w6 * Node2 + bias * bw4

    count = 0
    
    # function 
    # Node 3 and 4
    Node3 = 1 / (1 + math.exp(-array3))
    
    # Node 2
    Node4 = 1 / (1 + math.exp(-array4))
    





    array5 = w7 * Node3 + w8 * Node4 + bias * bw5
 
    # Node 5
    Node5 = 1 / (1 + math.exp(-array5))
    # The predicted Y values are stores at node 5
    
    graph1[index] = Node5
    index += 1

    
#count = 0
#while count < len(graph1):
#    graph1[count] = graph1[count] * 100
#    count += 1

plt.xlabel('TestedXvalues')
plt.ylabel('TestedYvalues')
plt.scatter(Test_Xvalues, graph1)
#plt.scatter(Test_Xvalues, Test_Yvalues)



     


















    
    


















    
    

















    
    






length of input testing
length of output testing
length of input training
length of output training
total length of training and testing


100
100
400
400
500














    
    

















    
    















    
    

















    
    















    
    

















    
    






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-0.006943559777926255
print Test_values length
100
print graph1 length
100










    
    
Out[67]:








<matplotlib.collections.PathCollection at 0x1dd9f6c9490>










    
    



























In [ ]: